Web-enabled Remote Spindle Monitoring and

Web-enabled Remote Spindle Monitoring and Prognostics Dragan Djurdjanovic, Jihong Yan, Hai Qiu, Jay Lee, Jun Ni NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems Center Univ. of Wisconsin-Milwaukee and Univ. of Michigan

Abstract: Today’s machine tool industries are facing unprecedented challenges brought about by development of outsourcing and low cost manufacturing in Asia. In addition, service and maintenance are becoming competitive technologies for companies to sustain their manufacturing productivity and customer satisfaction at the highest possible level in global market. A new business model, which is facilitating machine reliability and highquality service, is emerging. The revolution is being driven by Web-enabled remote technology and prognostic methods. Remote monitoring technologies enable a “zero” distance between the customers and equipment makers. Prognostic methods enable “near-zero” downtime performance and provide possibility of delivering just-in-time service. This paper gives an introduction about these technologies and methods and discusses their impacts to machine tool spindles. Key Words: Remote Monitoring, Web-enabled Technologies, Prognostics, Spindle Monitoring 1

INTRODUCTION

Today’s machine tool industries are facing unprecedented challenges brought about by development of outsourcing and low cost manufacturing in Asia. Manufacturing outsourcing provided many opportunities but also added challenges to produce and deliver products with improved productivity, quality, service and costs. Lead times must be cut short to their extreme extend to meet need the changing demands of customers in different regions of the world. Products are required to be make-to-order, which requires a tight control and near-zero downtime of the plant floor, equipment and devices. It necessitates suppliers to guarantee near-zero-downtime performance on factory equipment. Thus, service and maintenance are becoming competitive technologies for companies to sustain their manufacturing productivity and customer satisfaction at the highest possible level in global market. More and more manufacturing industries have realized these challenges and extended or shifted their business into service. A new business model, which is facilitating machine reliability and high-quality service, is emerging. Many industries have deployed “power by the hour” service business model with Long Term Service Agreement (LTSA) that guarantees near-zero downtime performance [1]. The revolution is being driven by Web-enabled remote technology and prognostic methods. This transformation requires advancements in predicting product performance throughout its life cycle in order to optimize the service, minimize and mitigate the risks, and ultimately reduce maintenance and operational costs and increase the profit margin.

2 2.1

ASSESSMENT AND PREDICTION OF PERFORMANCE DEGRADATION Needs for Performance Assessment and Prediction

The performance of machines and equipment degrades as a result of aging and wear, which decrease performance reliability and increase the potential for faults and failures. At the same time, the products and services must be of the highest quality to attain and retain a favorable market position. For example, one minute of downtime in an automotive manufacturing plant could cost as much as $20,000. Near-zero downtime and highest possible quality are fast becoming a necessity for both service and production enterprises. Reactive maintenance, performed only when equipment fails, results in both high production costs and expensive service downtime caused by equipment and process breakdowns. On the other hand, preventative maintenance is intended to eliminate machine or process breakdowns and downtimes through regular maintenance operations scheduled regardless of the actual state of the machine or process. Preventative maintenance intervals are determined using reliability theory and information about the machine or process lifecycle. This practice often results in an unnecessary loss of productivity either because maintenance is performed when the process or machine is still functioning at an acceptable level or because unpredicted breakdowns occur before scheduled maintenance operations are performed. Therefore, in contemporary markets, it becomes increasingly important to predict and prevent failures based on the current and past behavior of the equipment, thus ensuring its maintenance only when needed and exactly when needed. Performance forecasting enables a paradigm shift from the traditional “Fail and Fix (FAF)” approach to “Predict and Prevent (PAP)” practices. Therefore, just-in-time service and near-near downtime is no longer unrealistic. At the same time, rapid development of the web-enabled infotronics technologies provides a scaleable and easily deployable platform for remote monitoring and prognostics. 2.2

Watchdog Agent  for Performance Assessment and Prediction

Today, machines contain increasingly sophisticated sensors and their computing performance continues to accelerate. It is now possible to rapidly and accurately sense performance indicators, and thus assess and predict system performance. Under these circumstances, Condition-Based Maintenance (CBM), based on sensing and assessing the current state of the system, emerges as the most appropriate and efficient tool for achieving near-zero breakdown time through a significant reduction, and, when possible, elimination of downtime due to process or machine failure. Currently, the prevalent CBM approach involves estimating a machine' s current condition based upon the recognition of indications of failure [3]. Recently, several predictive CBM techniques within this failure-centered paradigm have been proposed. For example, a fuzzy logic neural network has been used to predict failure of a tensioned steel band with seeded crack growth [4]. Ray and Tangirala [5] built a stochastic model of fatigue crack dynamics in mechanical structures to predict remaining service time. Wang and Vachtsevanos [6], give an overview of different CBM algorithms and suggest a method to compare their performance for a specific application. These approaches notwithstanding, to implement the aforementioned predictive CBM techniques require expert and a priori knowledge about the assessed machine or process because the

corresponding failure modes must be known in order to assess the current machine or process performance. For this reason, CBM methods are application specific and nonrobust [7][8]. Recently, a new CBM paradigm based on the Watchdog Agent™ 1 was proposed for performance assessment and prediction [2],[7]-[10]. This paper addresses an innovative Watchdog Agent approach for multi-sensor performance assessment and prediction on the latter type of CBM. The Watchdog AgentTM bases its degradation assessment on the readings from multiple sensors that measure critical properties of the process, or machinery that is being considered. It is expected that the degradation process will alter the sensor readings that are being fed into the Watchdog AgentTM, and thus enable it to detect and quantify the degradation through quantitatively describing the corresponding change of sensor signatures. In addition, a model of the process or piece of equipment that is being considered can be used to aid the degradation process description, provided that such a model can be constructed. The prognostic function of the watchdog is realized through trending and statistical modeling of the observed sensor signatures and/or model parameters. This allows one to predict the future behavior of these patterns and thus forecast the behavior of the process, or piece of machinery that is being considered. Furthermore, the Watchdog AgentTM also has the diagnostic capabilities through memorizing the significant signature patterns in order to recognize situations that have been observed in the past, or to be aware of the situation that was never observed before.

Figure 1: Schematic representation of an Intelligent Maintenance System

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The Watchdog AgentTM has elements of intelligent behavior that enable it to answer the questions: •

When the observed process, or equipment is going to fail, or degrade to the point when its performance becomes unacceptable.

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“Watchdog Agent” is trademarked by the Center for Intelligent Maintenance Systems, a Multi-campus NSF Industry/University Cooperative Research Center at the University of Wisconsin-Milwaukee and the University of Michigan.

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Why the performance of the observed process, or equipment is degrading, or in other words, what is the cause of the observed process or machinery degradation. What is the most critical object, or process in the system with respect to maintenance, or repair;

This predictive information obtained from multiple interconnected products in the field, such as all machine tools in a factory or all elevator doors in the service area, could be utilized to make decisions based on the condition of the entire system that lead to optimal utilization of assets, necessary remote services exactly when they are needed and exactly where they are needed and ultimately to optimal operation with near-zero down-time. The aforementioned concept of Watchdog AgentTM based Intelligent Maintenance Systems is illustrated in Figure 1. Performance assessment functionality of the Watchdog Agent is based on obtaining relevant performance-related information hidden in multiple sensor readings. Through generic signal processing, feature extraction and sensor fusion techniques, this information is obtained from the signatures extracted from multiple sensor inputs [11][13]. Assessment of performance degradation is accomplished through modules performing processing of multiple sensory inputs, extraction of features relevant to description of product’s performance, sensor fusion and performance assessment. Performance assessment in this case involves matching the signatures that represent the most recent performance with those observed during normal system behavior. High correspondence between these two sets of signatures would indicate good performance, while a low match would indicate performance degradation and a need for maintenance. This match between the process signatures describing the normal process behavior and those describing the most recent process behavior is referred to as the performance Confidence Value (CV). Figure 2 gives a summary on different performance assessment tools developed by the Intelligent Maintenance Systems Center. More details about how these modules are employed for quantitative performance assessment can be found in [14].

Figure 2: Toolbox of solutions for the performance assessment functional module of the TM Watchdog Agent .

Over time, signatures describing various failure modes of the monitored equipment can be learnt so that they could be recognized next time such a failure mode

starts developing. Thus, the world of experiences recognized by the Watchdog Agent can grow and facilitate an ever-improving diagnostic function. As new signatures describing different failure modes are collected, the newly arrived process signatures can be matched not only against signatures describing the normal process behavior, but also against those describing each of the previously observed failure modes. These matches yield crucial diagnostic information revealing the reasons for the apparent performance degradation, depicted in the decreasing trend of the performance CVs. Thus, the diagnostic functionality of the Watchdog AgentTM is realized. Even though the performance CV already bares significant prognostic information about the remaining product’s useful life, additional prognostic information can be extracted by capturing the dynamics of the product’s behavior and utilizing it to extrapolate and predict the product’s behavior over time. This predictive and diagnostic information will ultimately lead to optimal maintenance policies and actions that will proactively prevent downtime. Forecasting techniques, such as Autoregressive Moving Average (ARMA) modeling based prediction [15] can be used to accomplish this task (see Section 2.4). The Watchdog Agent™ diagnostic and prognostic capabilities can be significantly improved with performance benchmarking because usually a considerable number of identical products are operating simultaneously in the market. This Peer-to-Peer (P2P) paradigm will improve the diagnostic and forecasting functionalities of the Watchdog Agent™. Performance assessment, forecasting and diagnostic functionalities of the Watchdog AgentTM, supported by the P2P paradigm of collaborative learning amongst similar “brother” products is illustrated in Figure 3. Multiple Sensors

P2P Sensory Sig. Proc.

Feature Extraction

Sensor Fusion

Perf. Evaluation

Performance Assessment

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Learning Prognostics and Diagnostics When? Why? Decision

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Figure 3: Peer-to-Peer Functionalities of the Intelligent Watchdog Agent™ .

2.3 Remote Spindle Performance Assessment-An Example An example of remote spindle monitoring of a vertical machining center will be presented in this paper. The performance assessment is accomplished using the sensor readings from three vibration sensors mounted near the spindle bearing. Data collection was triggered by the M-code form the CNC machine. Each vibration sensor reading was sampled at 10KHz and stored on the web-server for remote access and subsequent processing Figure 4 schematically shows the experimental setup.

The vibration signals were acquired while spindle was running idle and under normal condition. Each spindle operation cycle consisted of the spindle running without load at 5 different speeds for one second at each speed, with the following sequence of rotational velocities: 1. 2000rpm for one second 2. 4000rpm for one second 3. 8000rpm for one second 4. 4000rpm for one second 5. 2000rpm for one second In total, 2 hours and 12 minutes (735 continuous running cycles) of spindle operation signatures were obtained. Figure 5 summarizes the data acquisition setup used in the experiment, while Figure 5 shows typical vibration signals obtained from one CNC machine operating cycle.

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Figure 4: Experimental Setup for Remote Spindle Monitoring and Performance Assessment

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One cycle (length= 52100 samples)

Figure 5. Vibration Signals from one CNC Machine Operating Cycle

Sensor readings were decomposed into time and frequency localized energy coefficients using the wavelet packet transformation [17,18]. In this example, Daubechies wavelets were used to calculate the wavelet packet transform for each signal of one machine operation cycle. In total, 64 levels of frequency domain resolution were obtained by utilizing a 6-level wavelet packet decomposition. From the three vibration signals decomposed into the wavelet domain, Trial and error technique was used to extract a total of 13 frequency band energies that were indicative of the spindle performance, but were not contaminated by the noise. In essence, if one can acquire failure-related performance signatures, it is possible to compare the energy difference between the normal- and failure-related data sets in order to select an appropriate feature set. Signal energy from the selected wavelet packet frequency bands were used as inputs into multiple Logistic Regression classifier for performance assessment. Logistic regression is a technique for analyzing problems where there are one or more independent variables that determine an outcome that is measured with a binary variable (variable that can take only two values). Logistic linear regression models are of the form

y=

exp(α + β 1 x1 + β 2 x 2 + + β n x n ) 1 + exp(α + β 1 x1 + β 2 x 2 + + β n x n )

where x ( x1 x n ) denotes signal features and α and β ( β 1 β n ) are parameters that are obtained through maximum-likelihood estimation, using each of the known model data sets. Figure 6 shows the performance Confidence Values (CVs) obtained from the acquired data, using the Logistic Regression classifier.

Confidence Value

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Operating Cycle number Figure 6: Spindle Performance Confidence Values Obtained using the Wavelet Packet Decomposition and Logistic Regression

2.4

Performance Prediction and Forecasting

Figure 7 shows the concept and preliminary results of predicting the behavior of performance related process signatures using ARMA modeling techniques. Load sensor readings from a boring machine spindle have been remotely collected and processed into Joint Time-Frequency (TF) distributions. Performance related signatures were extracted from the TF distributions using the TF moments and Principal Component Analysis [10]. ARMA modeling techniques were then utilized to predict the behavior of the extracted principal components, as indicated in Figure 7. More details about the aforementioned methods of performance assessment and prediction will be given in future publications. /

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Figure 7: Spindle Feature Prediction Using ARMA Approach

3

ISSUES ON WEB-ENABLED PLATFORM MONITORING AND PROGNOSTICS

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REMOTE

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The rapid development of the web-enabled technologies is an important enabler for remote monitoring and prognostics. One critical issue in data transmission is the development of data transmission standards. The international standard organization (ISO) is also working on the topic, like ISO TC 184 Industrial Automation Systems and integration working group. The existing and emerging standards provide a solid foundation for system integration and collaboration. According to the standard for Open System Architecture for Condition-Based Maintenance (OSA-CBM), a typical remote monitoring and prognostics systems should consists of the following seven layers: • Sensor Module • Signal Processing • Condition Monitoring • Health Assessment • Prognostics

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Decision making support

Besides getting data from extra sensors, another attractive approach is to get data from machines directly. Some machine manufacturers have realized the requirement for connectivity from customers. Some equipment have built-in communication module (like the CNC machine tool from Hitachi-Seiki with UUP port). One of the major barriers is that most manufacturers adopt proprietary communication protocols which led to difficulties to connect diverse machine tools in factory floor suing a same remote monitoring platform. Currently, the IMS Center is developing a web-enabled remote monitoring deviceto-business (D2B) platform for remote monitoring of diversified machine tool spindles. Figure 8 shows a remote monitoring system for Hitachi Seiki CNC. The Hitachi-Seiki CNC machine has built-in data acquisition module, UUP port. A program was built to collect data from the machine through TCP/IP network.

Online monitoring

D2B™ Hitachi Seiki CNC Machine Status, error, operator input

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Hitachi-Seiki Proprietary Communication HS-MOS Library

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Figure 8: Architecture of Remote Monitoring of Hitachi Seiki CNC System

4

CONCLUSIONS

This paper discussed tools and methods for web-enabled remote monitoring and prognostics of machine tool spindles. The developed Watchdog Agent prognostics tools and web-enabled device-to-business (D2B) platform offer promising results and have been demonstrated in a number of real-life equipment. This paper briefly describes two case studies of remote spindle performance assessment and prediction in a real-life industrial environment. 5. ACKNOWLEMENT This work was support by the NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems (IMS) www.imscenter.net

REFERENCES [1] Jack Welch and GE, Business Week, Oct. issue 1996 [2] NSF I/UCRC Center for Intelligent Maintenance Systems, http://www.imscenter.net, 2002. [3] S. J. Engel, B. J. Gilmartin, K. Bongort and A. Hess, “Prognostics, the real issues involved with predicting life remaining,” Proc. of the IEEE Aerospace Conference Proceedings, 2000, Vol. 6, 2000, pp. 457-469. [4] D. C. Swanson, “A General Prognostics tracking algorithm for predictive maintenance,“ Proc. of the IEEE Aerospace Conference, Vol.6, 2001, pp. 2971 2977. [5] A, Ray and S. Tangirala. “Stochastic modeling of fatigue Crack Dynamic for OnLine Failure Prognostics,” IEEE Transactions on Control Systems Technology, Vol. 4, no. 4, July 1996, pp. 443-449. [6] G. Vachtsevanos and P. Wang “Fault prognosis using dynamic wavelet neural networks,” Proceedings of the 2001 IEEE International Symposium on Intelligent Control 2001 (ISIC ' 01)., 2001 pp. 79 -84. [7] J. Lee, “Machine Performance Monitoring and Proactive Maintenance in Computer-Integrated Manufacturing: Review and Perspective”, Int. J. Computer Integrated Manufacturing, Vol. 8, No. 5, 1995, pp. 370-380. [8] J. Lee, “Measurement of Machine Performance Degradation using a Neural Network Model”, Computers in Industry, Vol. 30, 1996, pp. 193-209. [9] N. Casoetto, D. Djurdjanovic, R. Mayor, J. Lee and J. Ni, “Multisensor Process Performance Assessment Through the Use of Autoregressive Modeling and Feature Maps,” to appear in Trans. of the XXXI SME/NAMRI, Paper Number 198, 2003; also to appear in the SME Journal of Manufacturing Systems, 2003. [10] D. Djurdjanovic, J. Ni and J. Lee, “Time-Frequency Based Sensor Fusion in the Assessment and Monitoring of Machine Performance Degradation”, in the Proc. of 2002 ASME Int. Mechanical Eng. Congress and Exposition, paper number IMECE2002-32032. [11] L. D. Hall and J. Llinas (Eds.), “Handbook of Sensor Fusion”, CRC Press, 2000. [12] L. D. Hall, “Mathematical techniques in Multi-Sensor Data Fusion”, Artech House Inc., 1992. [13] A. N. Steinberg, C. L. Bowman, and F. E. White, Jr., “Revisions to the JDL Data Fusion Model,” Proc. of the 3rd NATO/IRIS Conf., Quebec City, Canada, 1998. [14] D. Guido, G. Tong, D. Djurdjanovic, “BSU VA Gearbox Monitoring Prototype,” internal IMS document, 2002. [15] S. M. Pandit and S. M. Wu, “Time Series and System Analysis with Applications”, Krieger Publishing Company, 3rd Edition, 1993. [16] C. S. Burrus, R. A. Gopinath and H. Guo, “Introduction to Wavelets and Wavelet Transforms, a Primer”, Prentice Hall, Upper Saddle River, NJ, 1998. [17] D. G. Kleinbaum, “Logistic Regression”, Springer-Verlag, New York, 1994. [18] L. Cohen, “Time-Frequency Analysis”, Englewood Cliffs, NJ: Prentice Hall, 1995.