A Data-driven Prognostics Framework for Tool Remaining Useful Life ...

A Data-driven Prognostics Framework for Tool Remaining Useful Life Estimation in Tool Condition Monitoring Chong Zhang1 , Student Member, IEEE, Geok Soon Hong1 , Huan Xu1 , Kay Chen Tan2 , Fellow, IEEE Jun Hong Zhou3 , Hian Leng Chan3 , and Haizhou Li1 , Fellow, IEEE

Abstract— Tool Condition Monitoring (TCM) is an important topic in manufacturing industry, which improves product quality, production efficiency, reduces costs and downtime. This paper develops a new data-driven framework for estimating tool remaining useful life (RUL) in TCM. The framework includes the following modular components: data preprocessing with a proposed adaptive Baysian change point detection (ABCPD) for automatic data alignment, time window process, feature extraction, feature selection and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on a real-world gun drilling experimental dataset with multiple sensor measurements (i.e. thrust force, torque, 12 vibration signals). Different model selection, sensor selection, feature selection methods have been investigated in this paper. The simulation performance of the proposed framework is studied with the gun drilling dataset and it has been shown that the proposed framework has good performance.

I. I NTRODUCTION Tool condition monitoring (TCM) is a key enabler for unmanned machining systems. Combining with state-of-art sensing systems, signal processing techniques, pattern recognition techniques, machine learning algorithms together, TCM can achieve accurate and automatic tool condition estimation (e.g. tool wear or breakage) during operation process as well as enable the continuous manufacturing process. The aim of TCM is to give cost-effective prediction and detection to help provide replacement of a tool after maximum allowable wear. Prognostics is the study of how the tool condition degrade and the estimation of Remaining Useful Life (RUL) of the tool. With effective and reliable RUL prognostics, TCM can reduce overall downtime of the manufacturing processes and improve productivity so as to reduce the costs and increase the efficiency of production. This led a prevalent of TCM, which can prevent the occurrence of machine downtime and optimize the tool utilization in modern manufacturing process. Although prognostics plays an important role in TCM, the prognostics of TCM is still a lukewarm research area. Very few research have been done on prognostics of tool remaining useful life (RUL) estimation in TCM. 1 C. Zhang and H. Li are with the Department of Electrical and Computer Engineering, G. S. Hong and H. Xu are with the Department of Mechanical Engineering, National University of Singapore, 4 Engineering Drive 3, 117583, Singapore 2 K. C. Tan is with the Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong. 3 J. H. Zhou and H. L. Chan are with Singapore Institute of Manufacturing Technology (SIMTech).

Gun drilling is a kind of deep hole drilling process that uses a thin and long cutting tool to produce holes in metal at high-aspect ratio. Generally, the depth-to-diameter ratio (D : d) of gun drilling is more than 50. Gun drilling is commonly used in many industries such as oil and gas, aerospace, nuclear plant and so on. This paper aims to investigate the problem of estimating tool RUL for TCM. The case study used in this paper is gun drilling experiments with Inconel 718 materials. In previous data-driven TCM approaches [1]–[5] take instantaneous data samples as inputs. Such implementation assumes each data sample to be independent of each other. This paper investigates the effect of a sliding time window and take the time dependency between data samples into consideration. The entire proposed data-driven prognostics framework includes the following modular components: multi-sensor data acquisition, data preprocessing with an efficient and automatic adaptive Bayesian change point detection (ABCPD) algorithm to separate machining stages, time window process, feature extraction in time and frequency domains, feature selection and a multi-layer NN as the main machine learning algorithm to estimate RUL and the evaluation of the results. The rest of the paper is organized as follows. Section II introduces the background of TCM and gun drilling. Section III describes the details of the proposed prognostics framework for TCM. Section IV introduces the dataset used in this paper including its experimental setup and processes. Section V shows the simulation results of the proposed framework and studies about feature selection, sensor selection, benchmark modeling. Section VI concludes the research results. II. L ITERATURE R EVIEWS Data-driven TCM approaches [1]–[6] uses different machine learning techniques either to classify the health state of the tools or to predict the degradation trends of the tools. Neural network (NN) [7], support vector machine (SVM), fuzzy rule-based approaches [8], evolutionary algorithm (EA) [9], [10] based approaches have been implemented on TCM problems by many researchers. There are many other state-of-the-art machine learning approaches such as Deep Belief Networks (DBN) [11]–[13] have been applied in many industrial research areas (e.g. condition-based maintenance, machine health monitoring, etc.). Due to the high equipment and experimental conditions requirements of gun drilling, there are only few literature have done studies on TCM in

c 2017 IEEE 978-1-5090-6505-9/17/$31.00

gun drilling. Heinemann et al. [14] discussed the changes in different sensor signals (i.e. thrust force, torque, AE-RMS) with progressive tool wear of small diameter twist drills (D=1.5mm), when drilling boreholes (15mm) on plain carbon steel using MQL. Their experimental results reveal that large thrust force and AE signals signals appear to be much more suitable for TCM in this particular application then the thrust force. Jaako et al. [15] indicated that gun drill manufacturers recommend that the flank wear limit for regrinding should be 0.3 mm. After the limit is passed, regrinding is not cost-effective. The study in [16] found out the maximum outer flank wear of the gun drill was the best indicate tool condition. Sihvo et al. [17] used drilling feed force signals in time domain to estimate the tool wear with partial least square regression. The drawback of their regression method is that when cutting conditions change the corresponding parameters of the regression model should also change. Very rare researches have been done on tool RUL prognostics in TCM for machining process. Zhang et al. [18] implemented an indirect TCM approach with a wireless triaxial accelerometer for RUL prognostics in milling operations. The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and RUL. III. T HE P ROPOSED F RAMEWORK In this paper, we propose a novel data-driven prognostics framework of tool RUL estimation in TCM. The proposed data-driven prognostics framework includes multisensor data acquisition, data preprocessing, time window process, feature extraction, feature selection, modeling, target tasks and evaluation modules. The flowchart of the proposed data-driven prognostics framework is shown in Fig. 1. In data preprocessing, data alignment and data normalization are included. In order to detect multiple machining stages, an adaptive Bayesian change point detection (ABCPD) algorithm has been proposed for automatic data alignment. The simulation results showed the proposed ABCPD could give quite accurate and robust performances on real-world dataset. With time window process, the features have been extracted and selected based on data samples from each time window instead of each time instance. The features extracted based on time window can show more accurate and robust performances. Different sensor selection, feature extraction, modeling algorithms are investigated in this paper. A. Data Preprocessing 1) Data Alignment: In the real-world experiments, there are usually including many redundant data which may cause inaccurate data alignment and larger computational burden. It could result in worse performance of data-driven models. Since the task of RUL estimation of the tools only needs the sensor signals during the drilling stage, it is important to separate the signals during drilling stage from other operational stages (e.g. feed coolant stage, finish drilling and pull back stage, etc.). An adaptive Bayesian change point detection algorithm (ABCPD) has been proposed and used on the time series sensor data to detect the gun drilling stage.

After detecting different operational stages, data alignment will be conducted on multiple sensor signals. Change point detection is to find abrupt changes in time series data with intelligent algorithm. Many researches have used data mining, statistical learning, computational intelligence techniques to deal with change point detection problem. There are massive various types of real-world applications such as fraud detection, irregular motion detection in computer vision, sentiment analysis from Twitter data, event detection in stock market and so on. Currently, change point detection from high-dimensional time series data such as sensor fusion data has been attracting more and more attention. The key issue of change point detection is the identification of abrupt distribution changes at unknown data instants with a stochastic process. The aim of change point detection is to find the location of these changes and how many times do these happen. 2) Adaptive Bayesian Change Point Detection (ABCPD): As the drilling time of this application is known, an adaptive Bayesian change point detection algorithm has been proposed to detect the changes of machining states. Problem Formulation: Machining stages detection problem can be formulate into change point detection problem. During machining processes (e.g. drilling, turning, milling and etc.), when the machining stages changed, signals obtained by multiple sensors will change accordingly both in time domain and frequency domain. For instance, in gun drilling processes, Now we discuss change point detection problems from the perspective of statistics. First consider the hypotheses ”changed” and ”non-changed”. The hypotheses can be stated as follows. Given data samples X(t) = [x1 , x2 , · · · , xN ],t ∈ [1, T ], then the time series data samples X = [X(1)T , X(2)T , · · · , X(T )T ]T , X(t) ∈, where T is the total number of time instances and N denotes the total number of dimension of eachdata sample. H0 : f or 1 ≤ t ≤ T H1 : f or 1 ≤ t ≤ T

pθ0 (X(t)|X(t − 1), · · · , X(1)) pθ1 (X(t)|X(t + 1), · · · , X(T ))

(1)

The change point in the stationarity of X is defined as  H0 t < C∗ x(t) ∼ (2) H1 t ≤ C∗ where C∗ is the unknown change point and H0 6= H1 is the pdf characterizing the postchange distribution of the data. The Proposed ABCPD: A fixed-sized time window is used to move along time series data. If the dissimilarity between the distributions of two adjacent time windows besides a point is large enough, then this point is deemed as the change point. Here the variance is utilized as the dissimilarity indicator. If the variance difference between two adjacent time window, this point will be deemed as the change point. However, the time window size tw is quite hard to choose in different applications. In the proposed ABCPD algorithm, the time window size has been adaptively tuned based on the errors between known drilling time and estimated drilling time between two change points. The

Raw Signals

Data Preprocessing • •

Time Window Process

20 21 22 23 24 25 26

xˆt+1

xˆt

xˆt+4

···

St ep

18 19

Sliding Time Window

6

17

Ti m e

16

RMSE R2Score

t+

14 15

5

13

•

(a) Illustration of the detected change points by ABCPD on thrust force signals obtained during multi-stage machining process. (b) Illustration of the split sensor signals obtained during multi-stage machining (i.e. machine stand-by, giving coolant, drilling the workpiece) process.

t+

12

•

(b)

(a)

Fig. 2.

4

11

Remaining Useful Life Estimation

t+

10

3

9

t+

8

2

6 7

t+

5

•

Evaluation

Adaptive Bayesian Change Point Detection (ABCPD)

Input : X, the time series data,; [tlower , tup ] the range of time window size Output : The estimated change points Cˆ Initialization. Set the number of iteration G.; Set the estimated change points Cˆ = ∅.; Set the used time window size Ttw = ∅.; Randomly choose initial time window size tw .; Set threshold factor α.; Add tw into Ttw .; while g < G do while t ≤ T − tw do Calculate the variance σ (t) of Xˆ within each time window ˆ = [X(t), X(t + 1), · · · , X(t + tw )].; X(t) end while t ≤ T − tw do if |σ (t)| > α ∗ |σ (t − 1)| then Add t and σ (t) into Cˆ as t : σ (t); end end ˆ based on σ values descendingly.; Validation: sort(C) Estimate drilling time tˆdrilling between top two ttop1 and ttop2 ∈ Cˆ (i.e. tdrilling = |ttop1 − ttop2 |) ; if tˆdrilling = tdrilling then Terminate the while loop; ˆ Return C; else while tw ∈ Ttw do Randomly select a new time window size tw from [tlower , tup ]; end end end

1

4

Target Tasks

Time domain • PCC Frequency • MLP... • PCA domain The Proposed Data-driven prognostics framework.

t+

2 3

Modelling

•

Dimensions

1

Feature Selection

•

Data Alignment (ABCPD) Normalization

Fig. 1.

Algorithm 1:

Feature Extraction

t

Multi-sensor Data Acquisition

Fig. 3. Illustration of time window process with time window size of 2 (i.e. tw = 2).

details of the proposed ABCPD algorithm are shown in Algorithm. 1. Simulation Results of ABCPD: Based on the variance of the time series data, the larger differences between the distributions of the adjacent time windows can be detected as the change points. Fig. 2(a) shows the different machining stages of thrust force signals obtained during one drilling process have been split by ABCPD algorithm. It is clear that three change points indicating different machining stages had been detected by the proposed ABCPD algorithm. The sensor signals split by ABCPD during multi-stage machining process are shown in Fig. 2(b). The threshold factor α in ABCPD can be randomly chosen. In order to have faster convergence, the threshold factor α in this paper is set as 6 based on simulation results. The proposed algorithm has been tested on 20 gun drilling experimental trials, the average time error is 0.0394s with sampling frequency of 100Hz. 3) Data Normalization: Due to various conditions of realworld applications, the range of different signals or features have large variance. Therefore, it is important to normalized the data samples to the range of [0,1] prior to any train or test. The normalization was carried out within each feature, this will ensure to treat all features across all kinds of conditions equally. The data normalization is followed Norm(xm ) = m xm −xmin xm −xm , where m denotes each features of sensor signals. max

min

B. Time Window Process Time window process is to move a sliding time window along the timeline of multiple sensor signals and map the original data samples into processed data samples based on time window. Feature extraction and feature selection methods are implemented on time window processed data. Suppose T is the total number of time series data and M is the dimension number of each data sample, the original time series data samples are X = (x1 , x2 , · · · , xT ), where the t th data sample xt is (xt1 , · · · , xtM ). After time window process, the data sample becomes Xˆ = (ˆx1 , xˆ 2 , · · · , xˆ T−tw ). The t th data sample xˆ t becomes (xt , xt+1 , · · · , xt+tw−1 ), where tw denotes the time window size. An illustration of time window process is shown in Fig. 3. In general, it is suggested to choose the size of time window equaling to integral multiple of the number of data samples acquired during a full rotation of the spindle or the drive of the machine. The time window size is tw = N ∗ Sn60 ∗ fs , where Sn represents the spindle speed (rpm) and fs is the sampling frequency (Hz). N denotes the integral multiple. In this paper, the time window size tw is chosen as the number of data samples obtained during one full rotation of the spindle of gun drilling machine (i.e. N = 1). Because the gun drilling process is a cyclic rotation process, when the spindle rotates 360 deg, the significant characteristics of the signals will repeat again. Hence, in this paper, the size of the time window is chosen as the number of data samples

TABLE I T IME D OMAIN F EATURES . Feature

Definition

Feature

Maximum

xmax = max j=1,··· ,N x j

Kurtosis

Minimum

xmin = min j=1,··· ,N x j

Standard deviation

Mean

Root mean square

Log

1 ∑N x xµ = N j=1 j 1 ∑N log(1 + |x |) xlog = N j j=1

Variance

1 ∑N (x − x )2 xvar = N µ j=1 j

Clearance indicator

Range

xrange = xmax − xmin

Shape indicator

Skewness

xsk =

3 ∑N j=1 (x j −xµ ) (N−1)σ 3

Crest indicator

Impulse indicator

Definition 4 ∑N j=1 (x j −xµ ) xku = 4 (N−1)σ r 1 ∑N (x − x )2 xσ = N−1 µ j=1 j r 1 ∑N (x )2 xrms = N j=1 j max|x| xcl = r 1 ∑N (x )2 N j=1 j max|x| xcli = 1 ( ∑N (|x j |))2 rN j=1 1 N 2 N ∑ j=1 (x j ) xsi = 1 ∑N |x | N j=1 j max|x| xmi = 1 N N ∑ j=1 |x j |

TABLE II F REQUENCY D OMAIN F EATURES . Feature Maximum of band power spectrum Mean of band power spectrum Variance of band power spectrum Skewness of band power spectrum Kurtosis of band power spectrum

Standard deviation

Definition x( f )max = max x( f ) j 1 ∑N x( f ) x( f )µ = N j j=1 1 ∑N (x( f ) − x( f ) )2 x( f )var = N µ j j=1 3 ∑N j=1 (x( f ) j −x( f )µ ) x( f )SK = (N−1)σ 3 4 ∑N j=1 (x( f ) j −x( f )µ ) x( f )KU = 4 (N−1)σ s 2 ∑N j=1 (x( f ) j −x( f )µ ) x( f )σ = N−1

C. Feature Extraction Features are extracted from preprocessed signals in time, frequency, time-frequency domains. The aim of feature extraction is to extract the meaningful feature of tool wear. 1) Feature Extraction in Time Domain: In time domain, many statistical methods are applied on each time window to extract useful features such as maximum value, minimum value, mean, log, standard deviation, variance, range, skewness, kurtosis, root mean square, Crest indicator (CI), Clearance indicator (CLI), Shape indicator (SI), Impulse indicator (MI) and many other features have been commonly used in literature [8], [19], [20]. The extracted time domain features from the thrust force, torque and vibration signals are summarized in Table I. These features represent conspicuous statistical characteristics of the time series data obtained during machining operation processes. 2) Feature Extraction in Frequency Domain: Fast Fourier transform (FFT) and short-time Fourier transform (STFT) are commonly used in literature [19], [21] for analysing sensor data in frequency domain. Power spectrum is measured in the 0 to half of sampling frequency band. The extracted features in frequency domain including maximum, mean, variance, skewness, kurtosis of band power spectrum are summarized in Table II. D. Feature Selection From feature extraction process, many latent and observed features have been found. However, not all of these features are very important for the final task. Considering computational time and the curse of dimensionality, feature selection [22] are necessary to reduce dimension. Due to the dimension curse, it is better to reduce the irrelevant and redundant features. In this paper, two feature selection methods were experimented to determine the effectiveness of each feature

extraction method on this dataset. 1) Pearson’s Correlation Coefficient (PCC): Pearson’s correlation shows the linear relationship between two sets of data. Some researches [18] adopt PCC to select the optimal features for TCM in dry milling operation. PCC measures linear correlation between two or more variables. Given a sample, the sample Pearson’s correlation coefficient is defined as: pc = p

¯ − y) ¯ ∑ni=1 (xi − x)(y p i n ¯ 2 ∑ni=1 (yi − y) ¯2 ∑i=1 (xi − x)

(3)

where xi is from one sample set (e.g. feature set), yi belongs another sample set (e.g. corresponding tool wear state set), and n is the total number of samples. Positive PCCs denote positive linear correlation and negative PCCs denote negative linear correlation. If there is no correlation, PCC equals to zero. No correlation represents two variable do not have linear correlation. Confidence level of PCC represents how many features with larger correlation coefficient have been selected. In this paper, we set a confidence level (e.g. p = 0.80) in order to get rid of low pc value, i.e. only high correlated features are allowed to be selected as the final features. There are 120 features have been selected. 2) Principal Component Analysis (PCA): PCA [23] is an unsupervised feature extraction method which does not make use of class information of the samples. PCA works by capturing the variability of the data within the sample space and mapping the data into a lower dimension space. The new variables called Principal Components (PCs) were chosen such that the first PC has the highest variance and the subsequent PCs maximize variance while being orthogonal to the previous PCs. Due to its ease of application, PCA is often used as a basic dimension reduction tool before other feature extraction methods are implemented. In another word, PCA can find the projection direction such that the variance of projected data is maximized. Intuitively, find the intrinsic subspace of the original feature space in terms of retaining the data variability. E. Degradation Modelling Algorithm There are many existing algorithms that can be implemented as the degradation modeling algorithms, such as linear or non-linear regression methods, neural networks (NNs), support vector machines (SVMs), deep belief networks (DBN), multi-objective deep belief networks ensemble (MODBNE) [11], [13], switching Kalman filter (SKF) [24] and so on. In this paper, a basic Multi-Layer Perceptron (MLP) is used as a regressor to estimate the RUL of the tools. The MLP were implemented using the Neural Networks Toolbox in MATLAB environment. The implemented MATLAB codes and detailed explanation of PCA are published in [25]. The network structure used for all the data was 3 hidden layer with a random hidden neuron number in the range of [5, 50]. The inputs of MLP are the selected features at each time window and the output of MLP is the estimated RUL value of the corresponding train and test data respectively. The RUL function used in this paper is linear RUL function used in [26]. The performance of the basic

multi-layer neural networks will serve as a benchmark to evaluate the effectiveness of the proposed method. F. Performance Evaluation 1) Root Mean Square Error: A most popular evaluation metric, i.e. Root Mean Square Error (RMSE) of the estimated tool wear, is used as a performance metric. s RMSE =

1 N

TABLE III D ETAILS OF SENSOR TYPES AND MEASUREMENTS . Sensor Type

# Sensors # Channels per sensors Total # Channels Measurements Frequency (Hz)

(4)

i=1

where y denotes the true target value and yˆ represents the estimated target value. N is the total number of data samples. 2) R2Score: R2Score is the coefficient of determination of regression score function. The best possible R2Score is 1.0 and it can be negative. A constant model which always predicts the expected value of y, disregarding the input features, would get a R2Score of 0.0. R2Score is an asymmetric function with Eq. 5. ∑N (yi − yˆi )2 R2Score = 1 − i=1 ¯2 ∑Ni=1 (yi − y)

where y¯ is the mean of the observations, as y¯ =

(5)

1 N

∑Ni=1 yi .

IV. DATASET The real-world experimental dataset used in this paper is the gun drilling experiments conducted on a UNISIG USK25-2000 gun drilling machine in Advanced Manufacturing Lab from National University of Singapore, Singapore. A. Experimental Setup In the experiments, an Inconel 718 workpiece with the size of 1000mm ∗ 100mm ∗ 100mm is machined using gun drills. The tool diameter of gun drills is 8mm. The detailed tool geometry of the tools are shown in Table IV. Four vibration sensors (Kistler Type 8762A50) are mounted on the workpiece in order to measure the vibration signals in three directions (i.e. x, y and z) during the gun drilling process. The details about sensor types and measurements are shown in Table III. The sensor signals are acquired via a NI cDAQ9178 data acquisition device and logged on a laptop Dell Latitude E5450 with Intel Core i7-5600U 3.20GHz CPU, 16GB 2133MHz DDR4 RAM, 1TB SATA HDD. In data acquisition, 14 channels of raw signals belonging to three types were logged. The measured signals include force signal, torque signal, and 12 vibration signals (i.e. acquired by 4 accelerometers in x, y, z directions). The tool wears have been measured using Keyence VHX-5000 digital microscope. In this paper, the maximum flank wear has been used as the health indicator of the tool. In this dataset, it was found 3 out of 20 tools were broken, 6 out of 20 tools had chipping at final state and 11 out of 20 tools were worn after gun drilling operations. The machining operation is carried out with the detailed hole index, drill depth, tool geometry, tool diameter, feed rates, spindle speeds, machining times and tool final states are shown in Table IV. The drilling depth is 50 mm in zaxis direction. The tool wear is captured and measured by Keyence digital microscope. The tool wear is measured after each drill during gun drilling operations.

Force and Torque Sensor Dynamometer embedded in USK25-2000 machine 1 2 2 Thrust force and torque 100

TABLE IV

Microscope Keyence VHX-5000 Digital Microscope Tool wear -

E XPERIMENTAL C ONDITIONS

N

∑(yi − yˆi )2

Vibration Sensor Kistler 50g 3-axis accelerometer Type 8762A50 4 3 12 Vibration X,Y,Z 20000

Description

Hole Index H1 H1 H2 H2 H2 H3 H3 H3 H3 H3 H3 H3 H3 H3 H3 H4 H4 H4 H5 H5

01 02 01 02 03 01 02 03 04 05 06 07 08 09 10 01 02 03 01 02

Drill Depth (mm) 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50

Tool Geometry 1450mm N4 R9 1450mm N4 R1 1450mm N4 R1 1650mm N8 R1 1450mm N8 R9 1450mm N8 R9 1650mm N8 R9 1650mm N8 R9 1650mm N8 R1 1219mm N8 R9 1450mm N8 R9 1450mm N8 R9

Tool Diameter (mm) 8.05 8.05 8.05 8.05 8.02 8.05 8.05 8.05 8.05 8.05 8.02 8.02 8.02 8.02 8.02 8.08 8.08 8.08 8.045 8.045

Spindle Speed (rpm) 1200 1200 800 800 1650 1650 1650 1650 1650 1650 1650 1650 1650 1650 1650 1650 1650 1650 1650 1650

Feed Rate (um/rev) 20 20 20 20 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16

Machining Time (s) 125.00 125.00 187.50 187.50 113.64 113.64 113.64 113.64 113.64 113.64 113.64 113.64 113.64 113.64 113.64 113.64 113.64 113.64 113.64 113.64

Tool Final State Chipping Broken Chipping Broken Worn Worn Worn Worn Chipping Chipping Worn Worn Worn Worn Chipping Worn Worn Broken Chipping Chipping

B. Experimental Processes In the experiments, The procedure of the experiments are schematically shown in Fig. 4. The details of the gun drilling cycle are as follows. 1) 2) 3) 4) 5)

Machine startup. Feed internal coolant through coolant hole of gun drill. Start to drill through the workpiece. Finish drilling and pull the tool back. Machine shutdown.

The internally-fed coolant will exhaust the heat generated during gun drilling process and give high accuracy and precision performance. Machine Start

Feed Coolant

Fig. 4.

Gun Drilling

Finish Drilling & Pull Back

Machine Shutdown

The procedure of gun drilling experiments.

V. S IMULATION R ESULTS In this paper, the dataset is randomly split into train, validation and test sets. The training ratio is 0.7, the validation ratio is 0.15 and the testing ratio is 0.15. All the simulations have been done for 10 trials. A. Comparison of Different Models In order to study the effects of different degradation models, 10 different regression algorithms, i.e. multilayer perception (MLP), gradient boosting regressor (GB), extreme learning machine (ELM), support vector machine (SVM), ridge regression (RR), Lasso, AdaBoost regressor (AdaBoost), Stochastic Gradient Descent Regressor (SGD), ElasticNet (EN) and least angle regression (LAR), have been implemented with the same data inputs (i.e. feature extracted from all sensor signals). Gradient boosting (GB) [27] is an ensemble of decision trees with a boosting based combination scheme. ELM is a single hidden layer feed-forward neural network with randomized connection weights between the input and hidden layers and analytically determined connection weights between the hidden and output layers [28]. SVM [29] is one of the most popular supervised learning techniques which constructs a class separation hyper-plane

B. Effects of PCCs Confidence Levels

dimensions. The larger the PCA dimension is, the longer average running time and higher variance of running time of the models will be. By considering both accuracy, robustness and computational efficiency, the number of PCA dimension is chosen to be 113 in this paper. RMSE of the dataset with different PCA dimensions 220 train valid test 200

180

RMSE

160

140

120

100

80 8

15

22

29

36

43

50

57

64

71

78

85

92

99 106 113 120 127 134 141 148 155 162 169 176 183 190 197 204 211 218 225 232 239 246 253 260 267 274 281 288 295 Number of PCA Dimensions

Fig. 5. The RMSE values obtained by 3 hidden-layer MLP with fixed hidden neuron numbers (i.e. [10, 20, 10]) with different PCA dimensions in 10 trials using the gun drilling dataset.

R2Score of the dataset with different PCA dimensions 0.94

0.92

0.9

0.88

R2Score

in a high-dimensional space implicitly defined via a certain kernel function. RR [30] is a linear least squares with l2 regularization. LASSO [31] is a linear regression model with an l1 regularizer. Extra tree regressor (ETR) is a metaestimator with randomized decision trees. AdaBoost [32] is a meta-estimator by fitting an ensemble to the dataset while adjusting the ensemble weights according to the current errors. SGD [33] is using an efficient stochastic gradient descent learning approach to fit convex loss functions to fit linear regression models. EN [34] is a linear regression with combined l1 and l2 regularizer. LAR [35] is a kind of forward stepwise regression algorithm to find predictors who are most correlated with the targets. The comparison test results of different degradation models to predict RULs are shown in Table VI. Compare RMSE values obtained among different models, MLP gave the best performance with the lowest average RMSE value. GB obtained the best performance in terms of R2Score. Comparing computational time, SGD is the best with smallest average running time. This study can provide baseline performances of some benchmark models.

0.86

0.84

0.82

The effects of features selected based on different PCC confidence level have been investigated in this section. The simulation results of MLP with the features selected based on different confidence levels (i.e. 1, 0.95, 0.9, 0.8, 0.7, 0.6) are presented in Table. VII. The simulation results reveal that higher the confidence level is, better the prediction performance (i.e. lower RMSE values and higher R2Score values) of MLP is. Table. VII presents the performances of MLP with PCC feature selection method with different confidence level p in range of [0.6, 1] on train, validation and test datasets in terms of RMSE and R2Score. PCC with confidence level p = 0.8 shows the best performance on both validation and test data than others. Therefore, the confidence level of PCC used in this paper is chosen as 0.8. C. Effects of PCA Dimensions The effects of different PCA dimensions are studied in this section and their RMSE and R2Score results are shown in Fig. 5 and Fig. 6 respectively. Fig. 5 and Fig. 6 show the train, validation and test results obtained by three-hiddenlayered MLPs with different number of PCA dimensions within 10 trials. During 10 trials, the number of hidden neurons were fixed as [10, 20, 10] which was randomly selected from the range of [5, 50]. It can be obviously observed from Fig. 5 that with the increasing number of PCA dimensions the RMSE values become larger. It indicates that when the input dimension becomes too large, the performance would become worse. This may because of the curse of dimensions. In Fig. 6, it can be observed that the variances of R2Score with small number of PCA dimensions are much larger than those with high PCA dimensions. It indicates that the performances of regressors with small number of features are not be stable and robust enough. This may due to insufficient input information provided by the small number of input

0.8

train

0.78

valid test 0.76 8

15

22

29

36

43

50

57

64

71

78

85

92

99 106 113 120 127 134 141 148 155 162 169 176 183 190 197 204 211 218 225 232 239 246 253 260 267 274 281 288 295 Number of PCA Dimensions

Fig. 6. The R2Score values obtained by 3 hidden-layer MLP with fixed hidden neuron numbers (i.e. [10, 20, 10]) with different PCA dimensions in 10 trials using the gun drilling dataset.

D. Comparison of Different Feature Selection Methods Different feature selection methods have been compared in this paper. Table. VIII shows the comparison of the performances of MLP with different feature selection methods including MLP with PCC, MLP with PCA in terms of RMSE and R2Score with train, validation and test datasets respectively. The simulation results show that MLP with PCC feature selection methods outperforms other methods. E. Comparison of Sensor Selection Nowadays in many industry applications, sensor selection is very important and widely used in order to save costs and convenient to implementation. In order to verify the effects of sensor selection, the simulations of MLP with different kinds of sensors have been conducted in this section. There are two kinds of sensors have been used in this experiment, i.e. dynamometer and accelerometer. The force and torque signals are obtained from the same dynamometer while 12 vibration signals are taken from 4 accelerometers. Therefore, five different combinations of sensor signals including individual force signal, individual torque signal, signals from accelerometers (i.e. 12 vibration signals), signals from dynamometer (i.e. force and torque signals), all signals from both dynamometer and accelerometers (i.e. force, torque, vibrations) have been studied in this section. Simulation results including train, validation and test of MLP with different sensor selection are shown in Table V.

TABLE V C OMPARISON OF SIMULATION RESULTS OF MLP WITH DIFFERENT KINDS OF SENSOR INPUTS . Train Validation Test Model RMSE R2Score RMSE R2Score RMSE MLP-force 125.01 ± 16.55 0.5 ± 0.06 137.7 ± 19.66 0.44 ± 0.08 145.78 ± 26.02 MLP-torque 168.82 ± 11.19 0.32 ± 0.02 182.12 ± 15.13 0.28 ± 0.03 184.18 ± 13.28 MLP-vibration 126.15 ± 104.33 0.51 ± 0.25 142.16 ± 103.68 0.46 ± 0.25 158.18 ± 103.04 MLP-force-torque 106.79 ± 24.88 0.54 ± 0.07 137.65 ± 32.27 0.47 ± 0.11 130.65 ± 34.61 MLP-force-torque-vibration 119.35 ± 28.77 0.63 ± 0.08 133.22 ± 35.31 0.67 ± 0.10 128.9 ± 28.58

R2Score 0.46 ± 0.06 0.28 ± 0.03 0.41 ± 0.25 0.48 ± 0.11 0.67 ± 0.01

TABLE VI C OMPARE THE TEST RESULTS BETWEEN 10 DIFFERENT REGRESSION ALGORITHMS , I . E . MLP, G RADIENT B OOSTING R EGRESSOR (GB), EXTREME LEARNING MACHINE (ELM), SUPPORT VECTOR MACHINE (SVM), R IDGE R EGRESSION (RR), L ASSO , A DA B OOST R EGRESSOR (A DA B OOST ), SGDR EGRESSOR (SGD), E LASTIC N ET (EN) AND L EAST A NGLE R EGRESSION (LAR), IN TERMS OF RMSE, R2S CORE AND RUNNING T IME RESPECTIVELY. Models RMSE R2Score Running Time (s)

MLP 128.9±28.58 0.67±0.01

GB 319.22±217.11 0.81±0.24

ELM 394.02±67.21 0.8±0.07

SVM 432.65±145.81 0.74±0.17

RR 439.85±4.33 0.75±0

Lasso 468.22±22.72 0.72±0.03

AdaBoost 477.86±18.34 0.71±0.02

SGD 590.64±62.5 0.55±0.1

EN 693.05±85.44 0.38±0.14

LAR 703.54±57.4 0.37±0.11

6521.37±10084.52

323.17±479.02

1.9±1.16

661.84±175.79

1±0.03

11.52±1.4

214.96±169.18

0.22±0.01

1.93±2.72

1.06±0.07

TABLE VII C OMPARISON OF MLP WITH PCC FEATURE SELECTION METHOD WITH DIFFERENT CONFIDENCE LEVEL p IN RANGE OF [0.6, 1] ON TRAIN , VALIDATION AND TEST DATA IN TERMS OF

Confidence Level p 1 0.95 0.9 0.8 0.7 0.6

Train 105.87 ± 6.42 103.85 ± 16.23 101.83 ± 10.17 102.36 ± 10.62 105 ± 6.89 105.06 ± 8.57

RMSE AND R2S CORE .

RMSE Validation 126.22 ± 8.69 127.25 ± 17.17 118.69 ± 8.64 117.03 ± 7.88 122.39 ± 7.77 120.72 ± 8.76

Test 126.31 ± 6.03 123.43 ± 14.3 120.67 ± 11.03 118.71 ± 9.2 124.5 ± 8.18 122.6 ± 7.67

R2Score Validation 0.8579 ± 0.01 0.8565 ± 0.02 0.8668 ± 0.01 0.8688 ± 0.01 0.8619 ± 0.01 0.8657 ± 0.01

Train 0.8816 ± 0.01 0.8838 ± 0.02 0.8859 ± 0.01 0.8853 ± 0.01 0.8824 ± 0.01 0.8818 ± 0.01

Test 0.8578 ± 0.8611 ± 0.8647 ± 0.8666 ± 0.8607 ± 0.8638 ±

0.01 0.02 0.01 0.01 0.01 0.01

TABLE VIII C OMPARISON OF THE PERFORMANCES OF MLP WITH DIFFERENT FEATURE SELECTION METHODS IN TERMS OF RMSE AND R2S CORE WITH TRAIN , VALIDATION AND TEST DATASETS RESPECTIVELY. Different Feature Methods MLP-None MLP-PCC MLP-PCA

Train RMSE R2Score 105.8694 ± 6.42 0.8816 ± 0.01 102.3635 ± 10.62 0.8853 ± 0.01 107.2171 ± 9.97 0.8802 ± 0.01

The results show that MLP with all force, torque and vibration signals as the inputs have better performance in terms of both RMSE and R2Score. This indicates more signals from different kind of sensors would help to improve the performance of the degradation models since more useful information has been provided from multiple sensors. While the MLP with only force and torque as the inputs achieved the second best performance among five different signal combinations. Compare its RMSE values with the MLP with the inputs of individual force, individual torque, vibrations, the performance of MLP with force and torque as inputs has improved by 10.38%, 29.06% and 17.40% respectively. Comparing RMSE values obtained by MLP with all sensor signals and MLP with force and torque signals, the performance of MLP with all sensor signals has only improved 1.36%. It means that only torque signal, force signal or vibration signals cannot provide sufficient information of the health conditions of tools. There exists trade-off between the costs of sensors and the performance of the degradation models. The vibration signals contributes less to improve the performance of the degradation models compare to the cost reduction. Therefore, take the costs of the sensors and implementation conveniency into account, the vibration sensors can be reduced in practical applications.

Validation RMSE R2Score 126.221 ± 8.69 0.8579 ± 0.01 117.0346 ± 7.88 0.8688 ± 0.01 126.8 ± 10.71 0.857 ± 0.01

Test RMSE R2Score 126.31 ± 6.03 0.8578 ± 0.01 118.7091 ± 9.2 0.8666 ± 0.01 121.5664 ± 11 0.8623 ± 0.01

F. Computation Time Analysis Table VI reports the average computation time of 10 different regression algorithms, i.e. MLP, GB, ELM, SVM, RR, Lasso, AdaBoost, SGD, EN and LAR over 10 runs on gun drilling dataset with time window processing. It can be observed that SGD has the shortest average running time. Our experimental platform is a desktop PC with Intel Core i7-3770 3.40GHz CPU and 16GB RAM. The operating system is Windows 7. VI. C ONCLUSIONS This paper presents a new data-driven prognostics framework for estimating the RUL of tools in TCM. While in this paper the algorithm has been carried out on a real-world dataset collected from gun drilling processes. The proposed framework can theoretically be implemented to any other forms of similar manufacturing processes or mechanical systems. This framework uses a sliding time window and PCC feature selection to improve the performance of threehidden-layer MLPs. An adaptive Bayesian change point detection (ABCPD) algorithm has been proposed to detect different machining stages in order to achieve more accurate data alignment.

This study provides baseline performances of many benchmark models, i.e. multilayer perception (MLP), gradient boosting regressor (GB), extreme learning machine (ELM), support vector machine (SVM), ridge regression (RR), Lasso, AdaBoost regressor (AdaBoost), Stochastic Gradient Descent Regressor (SGD), ElasticNet (EN) and least angle regression (LAR). MLP performs best in terms of average RMSE. The effects of PCC confidence level have been studied in this paper. The confidence level of 0.8 presented the best performance comparing with other values. The effects of PCA dimension have also been investigated in this paper. With the increasing number of PCA dimension, the performance of the model would be decrease. While with too small number of PCA dimension, the variance of the performance is large and the robustness of the model is worse. The comparison of these two feature selection methods have been investigated in this paper. PCC outperforms PCA on the gun drilling experiment dataset. The effects of sensor selection have also been studied in this paper. Although more senors may have the better performance, less number of sensors could also achieve good performance by considering the trade-off between the sensor costs and the performance improvements. From the experimental results of TCM on gun drilling experiments, with only thrust force and torque signals from one dynamometer could give quite good performance comparing with involving all signals from dynamometer and accelerometers (i.e. thrust force, torque and vibrations). R EFERENCES [1] M. Zygmunt, M. Budyn, M. Orkisz, J. Ottewill, V. Jaramillo, and A. Nowak, “Visual modeling of condition monitoring systems,” in Emerging Technologies & Factory Automation (ETFA), 2012 IEEE 17th Conference on, pp. 1–4, IEEE, 2012. [2] S. Huang, K. M. Goh, K. C. Shaw, Y. San Wong, and G. S. Hong, “Model-based monitoring and failure detection methodology for ballnose end milling,” in Emerging Technologies and Factory Automation, 2007. ETFA. IEEE Conference on, pp. 155–160, IEEE, 2007. [3] W. Amer, Q. Ahsan, R. I. Grosvenor, and P. W. Prickett, “Machine tool condition monitoring system using tooth rotation energy estimation (tree) technique,” in Emerging Technologies and Factory Automation, 2005. ETFA 2005. 10th IEEE Conference on, vol. 1, pp. 8–pp, IEEE, 2005. [4] D. Dimla, “Artificial neural networks approach to tool condition monitoring in a metal turning operation,” in Emerging Technologies and Factory Automation, 1999. Proceedings. ETFA’99. 1999 7th IEEE International Conference on, vol. 1, pp. 313–320, IEEE, 1999. [5] A. P¨otsch, A. Berger, G. M¨ostl, and A. Springer, “Twecis: A testbed for wireless energy constrained industrial sensor actuator networks,” in Emerging Technology and Factory Automation (ETFA), 2014 IEEE, pp. 1–4, IEEE, 2014. [6] K. M. Goh, B. Tjahjono, and A. J. Aendenroomer, “A rapid configurable embedded development framework,” in Emerging Technologies and Factory Automation, 2007. ETFA. IEEE Conference on, pp. 135– 140, IEEE, 2007. [7] D. T. Nguyen, Q. B. Duong, E. Zamai, and M. K. Shahzad, “Bayesian network model with dynamic structure identification for real time diagnosis,” in Emerging Technology and Factory Automation (ETFA), 2014 IEEE, pp. 1–8, IEEE, 2014. [8] O. Yumak and H. Ertunc, “Tool wear condition monitoring in drilling processes using fuzzy logic,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4234 LNCS - III, pp. 508–517, 2006. [9] X. Qiu, J. Xu, K. Tan, and H. Abbass, “Adaptive cross-generation differential evolution operators for multi-objective optimization,” Evolutionary Computation, IEEE Transactions on, vol. PP, no. 99, pp. 1–1, 2015.

[10] A. Muruganantham, K. C. Tan, and P. Vadakkepat, “Evolutionary dynamic multiobjective optimization via kalman filter prediction,” IEEE Transactions on Cybernetics, vol. 46, pp. 2862–2873, Dec 2016. [11] C. Zhang, P. Lim, A. K. Qin, and K. C. Tan, “Multi-objective deep belief networks ensemble for remaining useful life estimation in prognostics,” Neural Networks and Learning Systems, IEEE Transactions on, 2016. [12] C. Zhang, K. C. Tan, and R. Ren, “Training cost-sensitive deep belief networks on imbalance data problems,” in Neural Networks (IJCNN), 2016 International Joint Conference on, pp. 4362–4367, IEEE, 2016. [13] C. Zhang, J. H. Sun, and K. C. Tan, “Deep belief networks ensemble with multi-objective optimization for failure diagnosis,” in Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on, pp. 32–37, Oct 2015. [14] R. Heinemann, S. Hinduja, and G. Barrow, “Use of process signals for tool wear progression sensing in drilling small deep holes,” International Journal of Advanced Manufacturing Technology, vol. 33, no. 3-4, pp. 243–250, 2007. [15] I. Jaako and J. Varis, “Tool condition monitoring in gundrilling using feed force and torque measurements,” in 19th International Conference on Production Research, Valparaiso, Chile, pp. 29–07, 2007. [16] I. Sihvo and J. Varis, “The wear of single flute gun drill and tool life tests,” Mechanika, vol. 73, no. 5, pp. 59–64, 2008. [17] I. Sihvo and J. Varis, “Estimation of tool wear of a gun drill using the signal curve pattern of feed force,” Mechanika, vol. 81, no. 1, pp. 71–78, 2010. [18] C. Zhang, X. Yao, J. Zhang, and H. Jin, “Tool condition monitoring and remaining useful life prognostic based on a wireless sensor in dry milling operations,” Sensors, vol. 16, no. 6, p. 795, 2016. [19] T. El-Wardany, D. Gao, and M. Elbestawi, “Tool condition monitoring in drilling using vibration signature analysis,” International Journal of Machine Tools and Manufacture, vol. 36, no. 6, pp. 687–711, 1996. [20] G. Byme and G. O’Donnell, “An integrated force sensor solution for process monitoring of drilling operations,” CIRP Annals - Manufacturing Technology, vol. 56, no. 1, pp. 89–92, 2007. [21] A. Kumar, J. Ramkumar, N. Verma, and S. Dixit, “Detection and classification for faults in drilling process using vibration analysis,” 2015. [22] Q.-G. Wang, X. Li, and Q. Qin, “Feature selection for time series modeling,” Journal of Intelligent Learning Systems and Applications, vol. 5, no. 03, 2013. [23] I. Jolliffe, Principal component analysis. Wiley Online Library, 2002. [24] P. Lim, C. K. Goh, K. C. Tan, and P. Dutta, “Multimodal degradation prognostics based on switching kalman filter ensemble,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, pp. 136– 148, Jan 2017. [25] L. I. Smith et al., “A tutorial on principal components analysis,” Cornell University, USA, vol. 51, no. 52, p. 65, 2002. [26] F. Yang, M. S. Habibullah, T. Zhang, Z. Xu, P. Lim, and S. Nadarajan, “Health index-based prognostics for remaining useful life predictions in electrical machines,” IEEE Transactions on Industrial Electronics, vol. 63, no. 4, pp. 2633–2644, 2016. [27] J. H. Friedman, “Greedy function approximation: a gradient boosting machine,” Annals of statistics, pp. 1189–1232, 2001. [28] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1, pp. 489–501, 2006. [29] B. S. Alex J Smola, “A tutorial on support vector regression,” Stat. and Comput., vol. 14, no. 3, pp. 199–222, 2004. [30] S. Le Cessie and J. C. Van Houwelingen, “Ridge estimators in logistic regression,” Applied statistics, pp. 191–201, 1992. [31] R. Tibshirani, “Regression shrinkage and selection via the lasso,” J. the Royal Statistical Society. Series B (Methodological), pp. 267–288, 1996. [32] G. R¨atsch, T. Onoda, and K.-R. M¨uller, “Soft margins for adaboost,” Machine learning, vol. 42, no. 3, pp. 287–320, 2001. [33] L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in Proceedings of COMPSTAT’2010, pp. 177–186, Springer, 2010. [34] H. Zou and T. Hastie, “Regularization and variable selection via the elastic net,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 67, no. 2, pp. 301–320, 2005. [35] B. Efron, T. Hastie, I. Johnstone, R. Tibshirani, et al., “Least angle regression,” The Annals of statistics, vol. 32, no. 2, pp. 407–499, 2004.