Intelligent Maintenance Infotronics System Platform for Remote Monitoring and E-Maintenance Zaifeng Chen Center for Intelligent Maintenance , University of Wisconsin at Milwaukee
Jay Lee Center for Intelligent Maintenance , University of Wisconsin at Milwaukee
[email protected]
[email protected]
Abstract - Internet and wireless communication technologies have made dramatic impacts on remote monitoring and maintenance. To enable equipment and products to achieve near-zero-downtime performance, predictive intelligence is needed to trigger service before product or equipment is failed. An infotronics platform, pioneered by the NSF Center of Intelligent Maintenance Systems (IMS), intertwines predictive informatics and embedded electronics intelligence to allow product and equipment to monitor their own performance degradation and autonomously initiate service call with synchronized spared parts ordering. This paper presents the architecture and functionalities of an intelligent maintenance infotronics system as well as discusses how prognostics logics can be embedded inside devices and how data can be exchanged among different systems with a standard format. The objective is to transform data into information and only send predictive information to the right place at the right time. Finally, two case study examples are used to illustrate the capabilities of the introduced infotronics platform. Keywords: infotronics, intelligent maintenance, prognostics, remote monitoring, e-maintenance
1
Introduction
Today’s manufacturing industries are facing unprecedented challenges brought about by development of outsourcing and lean manufacturing. Manufacturing outsourcing provides many opportunities but also challenges manufacturers to improve productivity, quality and service with less cost. Lead-time must be cut short to its extreme to meet the demands of customers in different regions of the world. Products are required to be make-toorder, which requires a tight control and near-zero downtime of the plant floor, equipment and devices. It also demands suppliers to guarantee near-zero-downtime performance on factory equipments. Thus, service and maintenance are becoming competitive technologies for companies to sustain their manufacturing productivity and
Hai Qiu Center for Intelligent Maintenance , University of Wisconsin at Milwaukee
[email protected]
customer satisfaction at the highest possible level in global market. On the other hand, more and more manufacturing industries are extending or transforming their business into service business model. Pioneered by GE, the service business model embraces the “power by the hour” business practices, with a Long Term Service Agreement (LTSA), to guarantee customers with a near-zero downtime operation performance [1]. This transition transforms the traditional maintenance from “fail and fix (FAF)” practices to a “prevent and predict (PAP)” paradigm [2]. The challenge is that how to reduce maintenance cost without sacrificing the maintenance service quality and customer satisfaction. NSF Center for Intelligent Maintennace Systems (IMS) has developed innovative infotronics-based predictive technologies and platform to address these issues [3]. infotronics technologies intertwine advanced predictive infomatics and embedded electronics systems intelligence to enable autonomous service business functions and objectives through the use of Internet and other tether-free technologies. There are three key components in the developed infotronics system, namely web-enabled remote monitoring platform, prognostic Watchdog Agent and Device-to-Buisness (D2B) synchronization agent. With the developed infotronics platform, the information on machine performance can be extracted from the data collected from machines or devices for predictive maintennace purpose at anywhere and anytime. Such useful information will be used to determine when a maintenance service should be required, what maintenance service will be required, and even how the service should be conducted. Then the service request will be directly sychronized with e-business system. For example, the scheduling system can be autonomously updated and prioritized based on the manufacture plan and the severity level of the maintenance request. In addition, relevant spare parts could be ordered autonomously through the Device-to-Business (D2B) transaction.
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Intelligent Maintenance
Infotronics System: Platform and Architecture The intelligent maintenance infotronics platform consists of five layers. From the bottom to the top, the first layer is the interface layer. This layer links equipment with the infotronics platform. It collects equipment parameters from sensors, equipment controllers or machine operators through DAQ system or human machine interface (HMI).
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The last but not least part is the synchronization module. The synchronization module bridges the equipment information system with the e-business system, e.g. CRM system. The module has two functions: •
Optimize predictive information and prioritize the associated into business decision, e.g. service requests
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Connect with e-business systems or emanufacturing systems to invoke certain business activity
The synchronization module can directly use information from the data transformation layer to trigger service request. Or the synchronization module uses the refined information from the intelligent tools to influent operational level of the business decision-making.
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Figure 1 Intelligent Maintenance Infotronics Platform and Architecture The data transformation layer is on the top of the interface layer. This layer will process the raw data provided by interface layer. The data process layer should be physically close to the interface layer to avoid unnecessary data transferring and storage. Above the data transformation layer, there is the data-transferring layer. It includes components store the information and to transfer it among different systems. Only information is transferred and stored for further utilization. The benefits for the rule are: •
Smaller data size, which makes the data storage and transferring much easier and more affordable.
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Normalized information, which make it possible to compare the information from the identical or similar equipment in different application environments.
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are used to perform performance degradation assessment and further data analyses or to conduct data mining to result in predictive intelligence about the degradation condition.
The data transformation layer can exclude data that might expose business confidence or privacy before it is really sent out.
On the basis of the data transferring layers, intelligent predictive informatics tools (described in the appendix)
In order to facilitate the data/information flow inside and outside the intelligent maintenance platform, the most cost-effective way is to adopt standards on data exchange. The selection of standards depends on the application environment, existing infrastructure and customer preference. Among those standards, OSA-CBM (Open System Architecture for Condition Based Maintenance) architecture [4] provides a very compelling standard framework. The existing and emerging standards (ISO TC 184) provide a solid foundation for system integration and collaboration. According to the standard for Open System Architecture for Condition-Based Maintenance (OSACBM), a typical remote monitoring and prognostics systems should consists of the following seven layers: • Sensor Module • Signal Processing • Condition Monitoring • Health Assessment • Prognostics • 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 builtin 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 many difficulties to connect diverse machine tools in factory floor using a same remote monitoring platform.
Case Study Example 1 - Web-enabled
E-Maintenance of An Elevator System The intelligent maintenance infotronics platform was validated in an elevator door system. Historically, 40 of elevator failures are caused by the malfunction of the door system. Innovation is needed to monitor the door system and provide performance degradation assessment remotely to enable just-in-time service.
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Figure 2. E-Maintenance of an Elevator System Fig.2 shows the system diagram of the integrtation of an infotromics system platform with a elevator door system. WAGO I/O module is selected as the interface layer that collects data from the elevator door controller and some extra sensors. The raw data is sent to the watchdog agent™ of the data transformation layer. Fig.3 shows the daily average open cycle time over 400 days, Fig. 4 shows the change of average maximum angular speed, and then probability of failure at each time point is calculated. At last, performance degradation can be assessed using a confidence value as shown in Figure 5. Cycle time (sec)
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Figure 6. Prediction of Probability of Failure This performance index is stored into database and is sent to remote monitoring system via the data transferring layer that consists of the OPC servers and a bridge program. The Watchdog Agent™ has a built-in prediction function. It can use the equipment performance index stored in database to predict when the door might reach to failure level (Figure 6). For data exchange, we adopted OPC protocol as the data exchange standard in the testbed. OPC is chosen since the OPC protocol is a popular protocol for building system control and monitoring. OPC is also recommended by OSA-CBM. When the machine performance index drops below certain threshold or a future possible failure is predicted, the Watchdog Agent will trigger a synchronization agent and the synchronization agent will invoke certain business transactions. The Watchdog Agent™ will send the machine information to the synchronization agent via RMI (Remote Method Invocation)[6]. Then the synchronization agent will connect with ORACLE workflow management server via ORACLE workflow API interface. The ORALE workflow management server is used as a business information hub. Through the workflow management server, we can direct the information to any other ebusiness system, such as Customer Relation Management (CRM) system, Enterprise Resource Planning (ERP) system, etc. The information flow can be configured in the ORACLE workflow builder based on the customer
enterprise policy or preference. The workflow management server also provides a mechanism to automate business decision-making based on machine information. When an equipment performance index dropped below certain criteria, the system logic will check whether the equipment needs emergency maintenance service. If so, the technician on duty will be notified at once via e-mail or pager. Otherwise, a notice will be sent to the maintenance manager to notify the manager to schedule a PM (predictive maintenance) for the elevator in the near future. The resources necessary to the future maintenance activity, like certain spare part, will be automatically allocated through the inventory management system. In the case an emergency maintenance service is required, the supervisor of that technician will be notified immediately as well. The supervisor will justify whether extra labor compensation is appropriate for this task. An additional payment request for the technician will be automatically sent to the accounting department if the supervisor approves it. In this example, IMS intelligent maintenance infotronics platform and infomatics tools enables the elevator system to request maintenance service autonomously before a costly breakdown really happens. Therefore, near-zero downtime can be achieved to satisfy the customers and cut cost for the service provider since the service provider can reduce unnecessary PM based on the information from the intelligent maintenance infotronics system.
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Figure 7: Architecture of Remote Monitoring of Hitachi Seiki CNC System Figure 8 schematically shows the experimental setup. Each vibration sensor reading was sampled at 10KHz and stored on the web-server for remote access and subsequent processing. 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 9 shows typical vibration signals obtained from one CNC machine operating cycle.
Case Study Example 2- Remote Machine Tools Spindle Performance Assessment
This case study demonstrate remote machine tool performance assessment 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. Figure 7 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.
Figure 8: Experimental Setup for Remote Spindle Monitoring and Performance Assessment
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One cycle (length= 52100 samples) Figure 9 Vibration Signals from one CNC Machine Operating Cycle Sensor readings were decomposed into time and frequency localized energy coefficients using the wavelet packet transformation [9,10]. 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
Confidence Value
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Figure 10 shows the performance Confidence Values (CVs) obtained from the acquired data, using the Logistic Regression classifier.
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Figure 10: Spindle Performance Confidence Values Obtained using the Wavelet Packet Decomposition and Logistic Regression Figure 11 shows the process and the 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 TimeFrequency (TF) distributions. Performance related signatures were extracted from the TF distributions using the TF moments and Principal Component Analysis [11] ARMA modeling techniques were then utilized to predict the behavior of the extracted principal components, as indicated in Figure 11. More details about the aforementioned methods of performance assessment and prediction will be given in future publications. Spindle Load of a Boring Machine
maximum-likelihood estimation, using each of the known model data sets.
Joint Time-Frequency Distribution
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5. Conclusions
Intelligent Watchdog Agent™ Tools
In the paper, an intelligent maintenance infotronics platform for remote monitoring and e-maintenance systems is introduced. Two case study examples were used to illustrate the developed tools and functionalities. With the introduced infotronics technologies, remote monitoring and e-maintenance tasks can be performed effectively. In addition, a new business mode that focuses on service and customer satisfaction becomes tangible [7].
Acknowledgement This work was supported by National Science Foundation Industry/University Cooperative Research Center on Intelligent Maintenance Systems (IMS) Center www.imscenter.net
Figure 12 Performance Assessment Modules •
Appendix: Predictive Informatics Tools The informatics tools used in the case study examples were introduced in the previous publications. This appendix gives a summary of these tools to augument the understanding of the predictive capabilities of the intelligent maintenance infotronics platform. The key component of the intelligent maintenance infotronics platform is the predictive infomatics tools [12] which are described as follows : 1).Smart Prognostics Algorithms (Watchdog Agent™) A neural network-based “digital doctor” inspired by biological perceptual systems and machine psychology theory, Watchdog Agent™ consists of embedded computational prognostic algorithms and a software toolbox for predicting degradation of devices and systems. A toolbox that consists of different prognostics tools has been developed for predicting the degradation or performance loss on devices, process, and systems. The algorithms include neural network based, timeseries based, wavelet-based and hybrid joint time-frequency methods, etc. (figure 12). Assessment of performance degradation is accomplished through several modules including performing processing of multiple sensory inputs, extraction of features relevant to description of product’s performance, sensor fusion and performance assessment. Each of these modules is realized in several different ways to facilitate the use of Watchdog Agent 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.
•
•
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Sensory processing module transforms sensor signals into domains that are most informative of product’s performance. Time-series analysis or frequency domain analysis could be used to process stationary signals (signals with time invariant frequency content), while wavelet, or joint time-frequency domains could be used to describe non-stationary signals (signals with timevarying frequency content). Most real life signals, such as speech, music, machine tool vibration, acoustic emission etc are non-stationary signals, which places strong emphasis on the need for development and utilization of non-stationary signal analysis techniques, such as wavelets, or joint time-frequency analysis. Feature extraction module extracts features most relevant to describing the product’s performance. Those features are extracted from the domain into which sensory processing module transforms sensory signals, using expert knowledge about the application, or automatic feature selection methods such as roots of the autoregressive time-series model, or time-frequency moments and Singular Value Decomposition. Decision-level sensor fusion is based on separately assessing and predicting process performance from individual sensor readings and then merging these individual sensor inferences into a multi-sensor assessment and prediction through some averaging technique. Performance evaluation module evaluates the overlap between most recently observed signatures and those observed during normal product operation. This overlap is expressed through the so-called Confidence Value (CV), ranging between zero and one, with higher CVs signifying a high overlap, and hence performance closer to normal. In case data associated with some failure mode exist, most recent performance signatures obtained through the signal processing, feature extraction and sensor fusion modules can be matched also against signatures extracted from faulty behavior data. This matching allows the Watchdog Agent to recognize and forecast a specific faulty behavior, once a high match with the failure associated signatures is assessed for the current process signatures, or forecasted based on the current and past product’s performance. Figure 13 illustrates this signature matching process for performance evaluation.
illustrates the aforementioned Watchdog Agent functionalities supported by the P2P communication and benchmarking paradigm.
Figure 13. Performance Evaluation using Confidence Value (CV)
Figure 15: Watchdog Agent and Peer-to-Peer Assessment Figure 16 summarizes a list of developed prognostics tools and their capabilities. Prognostics Approaches Experienced-Based
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• Detect abnormal conditions • Predict degradation • Estimate Remaining life based on risk distribution • Predict the trends • Enable P2P comparison & forecasting
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Realization of the performance evaluation module depends on the character of the application and extracted performance signatures. If significant application expert knowledge exists, simple but rapid performance assessment based on the featurelevel fused multi-sensor information can be made using the relative number of activated cells in the neural network, or using the Logistic Regression approach. For open-control architecture products, the match between the current and nominal control inputs and performance criteria can also be utilized to assess the product’s performance (figure 14). For more sophisticated applications with intricate and complicated signals and performance signatures, statistical pattern recognition methods, or Feature Map based approach are employed.
• Classification of faults & • Autonomous learning & new patterns generation
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Figure 14. Prognostics and Performance Forecasting Over time, as new failure modes occur, performance signatures related to each specific failure can be collected and used to teach the Watchdog Agent to recognize and diagnose that failure mode in the future. Thus, the Watchdog Agent is envisioned as an intelligent device that utilizes its experience and human supervisory inputs over time to builds its own expandable and adjustable world model. 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. Currently, Autoregressive Moving-Average (ARMA) modeling and Match Matrix methods are used to forecast the performance behavior. This entire infrastructure realizing multi-sensor performance assessment and prediction could be even further enhanced if Watchdog Agent are embedded on identical products operating under similar conditions could exchange information and thus assist each other in building the world model. Furthermore, this communication can be used to benchmark the performance of “brother-products” and thus rapidly and efficiently identify underperforming units before they cause any serious damage and losses. This paradigm of communication and benchmarking between identical products operating in similar conditions is referred to as the “peer-to-peer” (P2P) paradigm. Figure 15
Figure 16. A List of Prognostics Infomatics Tools and Their Capabilities
References [1] Jack Welch and GE, Business Week, Oct. issue 1996 [2] Service-Oriented Architecture Introduction, Part 1 By Michael Stevens, http://www.developer.com/services/article.php/1010451 [3] Lee, J. “Machine Performance Assessment Methodology and Advanced Service Technologies, Report of Fourth Annual Symposium on Frontiers of Engineering, National Academy Press, pp.75- 83, Washington, DC, 1999. [4] Open Standards for Condition-Based Maintenance and Prognostic Systems, Michael Thurston and Mitchell Lebold, 2001, http://www.osacbm.org/Documents/ConfPapers/MARCO N2001_OSACBM_FinalPaper.pdf [5] Jihong Yan, Muammer Koc and Jay Lee. Predictive algorithm for machine degradation detection using logistic regression. International Conference on MIM 2002: 172178, Sept. 9-11, Milwaukee, USA.
[6] Java Remote Method Invocation (RMI), http://java.sun.com/products/jdk/rmi/ [7] E-Maintenance and E-Business ,Jay Lee ,Wisconsin Distinguished and Rockwell Automation Professor ,Univ. of Wisconsin-Milwaukee&Director of Industry/University Cooperative Research Center on Intelligent Maintenance Systems (IMS) , http://www.uwm.edu/CEAS/ims/pdffiles/IMS%20Presenta tion.PDF
[8] Integration of Diagnostics and Maintenance Applications with a Manufacturing Control Application, Reference Integration Models and Interoperability Schemas, Proposed Collaboration between ISO TC108/SC5 and ISO TC184/SC5 , Rockwell Automation [9] D. G. Kleinbaum, “Logistic Regression”, SpringerVerlag, New York, 1994. [10] L. Cohen, “Time-Frequency Analysis”, Englewood Cliffs, NJ: Prentice Hall, 1995. [11] 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. [12] Djurdjanovic, D., Ni, J., and Lee, J., “Sensor Fusion for Time-Frequency Based Monitoring and Assessment of Process Performance,” submitted to Trans. of ASME, Journal of Manufacturing Science and Engineering, 2003.
