introduction of watchdog prognostics agent and its application to ...

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Journal of the Chinese Institute of Industrial Engineers, Vol. 22, No. 1, pp. 56-63 (2005)

INTRODUCTION OF WATCHDOG PROGNOSTICS AGENT AND ITS APPLICATION TO ELEVATOR HOISTWAY PERFORMANCE ASSESSMENT Jihong Yan and Jay Lee Center for Intelligent Maintenance Systems (IMS), Dept. of Industrial and Manufacturing Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI 53211 Yi-Cheng Pan Center for Activation and Space Technology Industrial Technology Research Institute (ITRI), Hsinchu, Taiwan

ABSTRACT Today’s competition in industry depends not just on lean manufacturing, but also on the ability to provide customers with accountable life-cycle support for sustainable value. To improve customer service responsiveness and aftermarket business efficiency, new service business model for enable products and systems to achieve near-zero unscheduled downtime has been adopted by many companies. This transformation necessitates the depolyment of smart prognsotics tools to predict and prevent possible failures before they occur. This paper introduces an innovative approach in using Watchdog AgentTM for machine degradation and failure prognostics. The methods of Watchdog AgentTM are developed to use multi-sensor information from product for performance degradation assessment and life prediction. Specially, the logistic regression (LR) method is introduced and applied to an elevator hoistway performance aseessment. The logistic regression model is designed for repeatable tracking of motor speed profile of elevator hoistway movement for on-line monitoring and prognostic purposes. Features such as acceleration time (interval from triggering elevator to reaching maximum speed), deceleration time (from maximum speed to elevator stop), as well as average maximum speed were used as inputs to Logistic Regression tool for performance assessment. The real application results show that the LR is a very promising methodology for machine degradation assessment. Keywords: prognostics, degradation assessment, life prediction, elevator maintenance

1. INTRODUCTION Today, machines contain increasingly sophisticated sensors and their computing performance continues to accelerate. Therefore, 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 an appropriate and efficient tool for achieving near-zero breakdown performance [1]. With a well-implemented CBM system, a company can save up to 20% in smaller production losses, improved quality, decreased stock of spare parts etc [2]. Currently, the prevalent CBM approach estimates a machine's current condition based upon

the recognition of indications of failure or early stage [3,4] diagnosis information, instead of dynamic degradation evaluation. Most CBM methods are application-specific and are not robust for an unknown application environment [5]. This paper introduces an innovative Watchdog Agent™ for continuous multi-sensor based machine performance degradation assessment and prediction, which provides generic prognostics algorithms and enables manufacturers and customers to rapidly assess elevator hoistway maintenance is demonstrated a product's performance and instantly figure out required maintenance activities. Its application to an.

Yan et al.: Watchdog Agent and Its Application to Elevator Hoistway Performance Assessment

2. WATCHDOG AGENTTM BASED PROGNOSTICS APPROACH FOR MACHINE PERFORMANCE DEGRADATION ASSESSMENT The Watchdog AgentTM is a prognostics tool which can assess machine performance degradation based on the readings from multiple sensors from a machine or system. The prognostic function of the Watchdog Agent™ is realized through trending and statistical modeling of the observed sensor signatures and/or model parameters. This allows us to predict the future behavior of these patterns and thus forecast the behavior of the machinery of process. Furthermore, the Watchdog AgentTM also possesses the diagnostic capabilities through memorizing the significant signature patterns so a faulty pattern or behavior can be identified when it occurs. In general, Watchdog AgentTM has elements of intelligence for the following questions: • When the observed process, or equipment is going to fail, or degrade to the point when its performance becomes unacceptable. • 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; Figure 1 depicts the functionalities of the Watchdog Agent™ and its role in an intelligent maintenance system. In summary, Watchdog Agent™ consists of the following modules: module for multi-sensor assessment of machine performance degradation, :

module for forecasting of performance degradation, and module for diagnosis or the reasons of performance degradation. Each of these modules is realized in several different ways to facilitate the use of Watchdog Agents in a wide variety of products and applications, with various requirements and limitations with respect to the character of signals, available processing power, memory and storage capabilities, limited space, power consumption etc. More details about how these modules can be found in [6-12].

3. PERFORMANCE ASSESSMENT OF AN ELEVATOR HOISTWAY SYSTEM – AN EXAMPLE To illustrate how Watchdog Agent™ can be used for machine performance degradation evaluation and predictive maintenance, logistic regression based algorithm, one of prognostics methodologies of Watchdog Agent™ is implemented in a real elevator predictive maintenance system. In this section, issues such as principal signals selection, feature extraction as well as logistic regression algorithm will be introduced in detail.

3.1 Principal signals selection In terms of elevator hoistway movement (upwards and downwards), the profile of the motor speed is very important for ride comfort. Steady speed profile is required to hold the car steady before and after the floor-to-floor run. Speed profile relates to both load balance and bearing frication:

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Figure 1. Watchdog Agent™ and its role in an intelligent maintenance system

Journal of the Chinese Institute of Industrial Engineers, Vol. 22, No. 1 (2005)

In tall buildings the weight of support cables (ropes) is also significant. These cables are routed from the top of the car and over the machine sheave at the top of the hoistway and down the side of the elevator shaft to hang the counterweight. As the elevator car travels upward, the length of cables on the elevator side of the sheave becomes shorter (lighter), and that on the other side of the counterweight becomes longer (heavier). This causes a significant shift in the load balance for ride control which necessitates the elevator motor to maintain proper speed and car position to avoid unbalance. Bearing frication is another issue. Sleeve type bearings on the machine supporting the weight of the elevator will typically settle during a long stop at a floor landing, causing some bearing lubrication to be squeezed out. As a result a significant amount of torque maybe required at the beginning of the run in order to break away high bearing friction. This may cause a noticeable spike in current at the start of an elevator run. Correspondingly, the speed profile will shift more or less. Besides motor speed signal, corresponding switch signals such as CAU (1: elevator move up), CAD (1: elevator move down), DZ (1:stay in door zone). CAU and CAD were also used to illustrate the direction of movement (upward or downward); DZ was selected to count the floor numbers of each call/service. Note that when the elevator car passes door zone, the DZ signal switches to 1. These signals were used to extract features which can represent the characteristics of floor-to-floor movements to eventually evaluate the elevator running performance. Figure 2 shows the DAQ system of this experimental setup. Three digital signals from controller and one speed signal from motor were acquired form the elevator hoistway system. Sampling rate is 20 Hz. The data set was gathered while the elevator was called randomly for daily usage.

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3.3 Logistic regression method

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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 dichotomous variable in which there are only two possible outcomes. The goal of logistic regression is to find the best fitting model to describe the relationship between the dichotomous characteristic of dependent variable and a set of independent variables. Here, logistic regression is used to setup the relationship between normal and failure/abnormal running conditions.

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first up then down with S-Curve type jerk control (fixed rate of change of acceleration). Note that the acceleration and deceleration profiles are usually identical. So the acceleration and deceleration rates and jerk in and jerk out portions of the S-Curve are the same, regardless of the direction of travel. Therefore it is reasonable to make use of this attribute, and extract the corresponding features which can represent the shape of this S-Curve from the profile as acceleration time (from triggering to maximum speed: T0-T1 or T4-T5), deceleration time (from maximum speed to ending: T2-T3, or T6-T7), as well as average maximum speed. The data used in this paper was acquired on Feb. 19, 2004, from 09:55:10 to 18:45:22, when the elevator was called randomly for daily usage. There are altogether 488 calls during the almost 9 hours including 246 upwards and 242 downwards movements. All the raw data were input to feature extraction module to extract the critical features like acceleration time, deceleration time and average maximum speed. Then performance was evaluated based on the features using logistic regression method. Finally confidence values are derived from logistic regression model and output dynamically at the end of each call/service, namely, a confidence value was delivered right after the elevator car stops to represent the current running condition of the elevator system.

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Figure 2. DAQ system

3.2 Feature extraction Figure 3 shows the typical velocity and acceleration profiles of elevator floor-to-floor runs,

Usually, the machine condition description from daily maintenance records/logs is a dichotomous problem (either normal or failure)

Yan et al.: Watchdog Agent and Its Application to Elevator Hoistway Performance Assessment

which can be represented using a logistic regression function. The logistic function is [13]: Prob(event)= P ( x ) =

1 e g( x) = −g( x) 1+ e 1 + e g( x)

(1)

Since P ( x ) ranges between 0 (normal) and 1 (failure), the logistic function can be treated as a probability distribution function. The S-shape of the function in Figure 4 indicates a relative low probability of occurrence until some threshold is reached, at which time the probability of occurrence increases rapidly.

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The approach is absolutely feasible when enough maintenance records including both normal and failure data are available to train the model. But usually, there is a lack of empirical data on which prognostic calculations can be based, tests are very difficult and expensive to perform. Namely, there is no enough historical failure data available so that solving parameters of α , β 1 , " , β k without enough historical data is a challenge. In this case, some data or knowledge provided by technicians or experts about abnormal/unacceptable is needed for implementing logistic regression mapping.

The logistic or logit model is: g ( x ) = log

P( x ) 1 − P( x )

(2)

where g ( x ) is a linear combination of the independent variables x1 , x 2 , " x k , or log [Prob(event)/Prob(no event)] = g (x) = α + β 1 x1 + β 2 x 2 + " + β k x k

(3)

The pre-condition for calculating P(x) is determining parameters α and β 1 , " , β k in advance. Due to the fact that the dichotomous dependent variable makes estimation using ordinary least squares inappropriate, rather than choosing parameters that minimize the sum of squared errors, estimation in logistic regression chooses parameters of α and β 1 , " , β k using maximum likelihood method. Then, the probability of failure for each input vector x can be calculated according to Eqn. 1. The goal of logistic regression is to estimate the k + 1 unknown parameters ( α , β 1 , " , β k ) in Eqn.3. For logistic regression, least squares estimation is not capable of producing minimum variance unbiased estimators for the actual parameters. In this case, maximum likelihood estimation is used to solve for the parameters that best fit the data. This is done with maximum likelihood estimation which entails finding the set of parameters for which the probability of observed data is greatest [14]. 1

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As introduced in preceding sections, features as acceleration time, deceleration time, and average maximum speed were extracted first of all from motor speed, CAD, CAU and DZ signals. The grand averages of features were calculated as the basis (normal) of logistic regression algorithm. By recognizing the deviation from normal behavior, the performance assessment has been done. Since in this case, failure or unacceptable data is not available, the failure points were set as according to operator’s experience. For example, if the grand average of acceleration time is 1.6 seconds (‘1’), the failure acceleration time (0) was set as either 0 seconds, or 3.0 seconds. Figure 5 shows the features of upward movement. Altogether 246 calls for going upstairs were made. Mean of the corresponding features: average acceleration time: 1.3245s; average deceleration time: 1.6613s; grand average max speed: 1795.1 rpm. Figure 6 illustrates the features of downward movement. Altogether 242 calls were made for going downstairs. Mean of the respective features: average acceleration time: 1.2888s; average deceleration time: 1.5944s; grand average max speed: 1795.4 rpm. Features of upward and downward movement were used for training separately. 50 pairs of features from upward feature set were used for training logistic regression model to solve parameters ( α up , β up1 , β up 2 , β up 3 ) for upward movements; the rest 196 pairs of features were used for validation purpose. Similarly, 50 pairs of features from downward feature set were used for training to calculate parameters ( α down , β down1 , β down 2 , β down 3 )

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for downward movements; the rest 192 pairs of features were used for validation.

0.4 0.2 0 -1 0

3.4 Training of logistic regression models and performance evaluation

-5

Figure 4.

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Journal of the Chinese Institute of Industrial Engineers, Vol. 22, No. 1 (2005)

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Figure 5. Features of 246 calls for going upward

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Figure 6. Features of 242 calls for going downward

Yan et al.: Watchdog Agent and Its Application to Elevator Hoistway Performance Assessment Figure 7 shows evaluation results of the overall 488 calls using different parameters of upward and downward movement, namely, treat upward and downward movement separately, if the movement is upward, parameter set α up , β up1 , β up 2 , β up 3 is used; if the movement is downward, parameters α down , β down1 , β down 2 , β down 3 are used. It is evident from figure 7 that analysis and dealing with the upward and downward movements separately is rational, since the data was acquired when the elevator was under normal condition, the assessment results (CV values) are stable (from 0.82 ~ 1.0). In summary, for elevator hoistway movement performance assessment, the upward and downward should be treated separately. Namely two logit models for upward and downward movements evaluation respectively are needed.

4. CONCLUSIONS This paper introduced the use of Watchdog Agent™ as a prognostics assessment tool for machine performance degradation assessment. This tool can be used to support predictive Condition-Based Maintenance practices to identify potential failure components with possible remaining useful life. An elevator hoistway system is used to illustrate how each module of the Watchdog Agent™ is performed. Remaining life prediction can also be performed as in [8] which is not addressed in this paper, the prediction module will be implemented to elevator predictive maintenance system in near future.

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REFERENCES 1. NSF I/UCRC Center for Intelligent Maintenance Systems, www.imscenter.net. 2. Bengtsson, M., “Condition Based Maintenance on Rail Vehicles”, IDPMTR 06 (2002). 3. Engel, S. J., B. J. Gilmartin, K. Bongort and A. Hess, “Prognostics, the real issues involved with predicting life remaining,” Proc. of the IEEE Aerospace Conference Proceedings, 6, 457-469(2002). 4. Kacprzynski, G. J. and M. J. Roemer, “Health Management Strategies for 21st Century Condition-Based Maintenance Systems”, International COMADEM Congress, Houston, TX, December(2000). 5. Greitzer, F. L., E. J. Stahlman and T. A. Ferryman, “Development of a Framework for Prediction Life of Mechanical Systems: Life Extension Analysis and Prognostics”, SOLE 1999 Symposium, Las Vegas, Nevada(1999). 6. Casoetto, N., 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 SME/NAMRI, Paper 198(2003) 7. Lee, J., “Machine Performance Monitoring and Proactive Maintenance in Computer-Integrated Manufacturing: Review and Perspective”, Int. J. Computer Integrated Manufacturing, 8(5), 370-380(1995).

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Journal of the Chinese Institute of Industrial Engineers, Vol. 22, No. 1 (2005)

8. Lee, J., “Measurement of Machine Performance Degradation using a Neural Network Model”, Computers in Industry, 30, 193-209(1996). 9. Djurdjanovic, D, J. Ni and J. Lee, “Time-Frequency Based Sensor Fusion in the Assessment and Monitoring of Machine Performance Degradation”, to appear in the Proc. of 2002 ASME Int. Mechanical Eng. Congress and Exposition, paper number IMECE2002-32032(2002). 10. Yan, J., M. Koc and J. Lee, “Predictive Algorithm for Machine Degradation Detection Basd on logistic Regression,” Proc. of 5th International Conf. on Managing Innovations in Manufacturing(2002). 11. Tong, G., M. Koc and J. Lee, “System Performance Assessment Based on Control System Criteria Under Operating Conditions,” Proc. of 5th International Conf. on Managing Innovations in Manufacturing(2002). 12. Wang, X., G. Yu, M. Koc and J. Lee, “Wavelet Neural Network for Machining Performance Assessment and Its Implications to Machinery Prognostics”, Proceedings of the 5th International Conference on Managing Innovations in Manufacturing (MIM)(2002). 13. Spezzaferro, K. E., “Applying logistic regression to maintenance data to establish inspection intervals”, Proc. Annual Reliability and Maintainability Symposium, 296-300(1996).

14. Czepiel, S. A., Maximum likelihood estimation of logistic regression models: theory and implementation. http://czep.net/stat/mlelr.pdf.

ABOUT THE AUTHORS Jihong Yan is a postdoctoral research associate of Center for IMS, Department of Industrial & Manufacturing Engineering, University of Wisconsin-Milwaukee, USA. Her research interests include intelligent methods for predictive maintenance, optimization scheduling methods, concurrent engineering, systems modeling, simulation, and intelligent algorithms. Jay Lee is Wisconsin Distinguished Professor and Rockwell Automation Professor at the Univ. of Wisconsin-Milwaukee, director of center for IMS. His current research is in the areas of intelligent maintenance and self-maintenance systems. He has pioneered Watchdog Agent™ embedded prognostics technologies and web-enabled Device-to-Business (D2B)™ platform for predictive machine degradation assessment, remote monitoring, and prognostics. He is a Fellow of SME, also a Fellow of ASME. Yi-Cheng Pan is an engineer of center for activation and space technology, industrial technology research institute (ITRI), Hsinchu, Taiwan.

Yan et al.: Watchdog Agent and Its Application to Elevator Hoistway Performance Assessment

Watchdog代理人在電梯垂直通路的應用 Jihong Yan and Jay Lee 美國威斯康辛大學工業與製造工程學系 Yi-Cheng Pan 工業技術研究院

摘要 今日工業的競爭除了依賴精實生產之外，還得依賴能夠提供顧客在產品生命週期中具 一定水平價值之責任性支援的能力。為了改進客服反應力及修配用零件市場之商業效 率，已有許多公司採納新系統與產品的服務商業模型以使未規劃之停工期達到近乎零 的境界。這種轉變使得能夠在失效可能發生前，進行預測及預防作業之智慧型預測工 具變得十分必要。本論文介紹一種使用watchdog代理人來做機器退化及失效預測的創 新方法。Watchdog代理人的方法使用多重感應器資訊，用於產品之績效退化評估與生 命預測。特別是邏輯迴歸法，用於電梯之垂直通路中馬達速度描述之反覆追蹤。特徵 值如加速時間、減速時間及平均最大速度當成邏輯迴歸法做績效評估的輸入值。實際 應用結果顯示邏輯迴歸法是在做機器退化評估中一種非常大有可為的方法。 關鍵詞：預言，退化評估，生命預測，電梯維修

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