Product Lifecycle Knowledge Management Using ...

Int. J. Knowledge Engineering and Data Mining, Vol. 1, No. 1

Product Lifecycle Knowledge Management Using Embedded Infotronics: Methodology, Tools, and Case Studies Jay Lee* Ohio Eminent Scholar and L.W. Scott Alter Chair Professor Director of the Center for Intelligent Maintenance Systems (IMS) University of Cincinnati PO Box 210072, Cincinnati, OH 45221 Email: [email protected] Fax: +1-513-556-4647 *Corresponding Author

Mohamed AbuAli Research Assistant Center for Intelligent Maintenance Systems (IMS) University of Cincinnati PO Box 19950, Cincinnati, OH 45211 Email: [email protected]

Chiao-Lin (Rene) Deng Visiting Researcher from National Taiwan University of Science and Tech. Center for Intelligent Maintenance Systems (IMS) University of Cincinnati PO Box 210072, Cincinnati, OH 45221 Email: [email protected]

Chia-Hsi (Janet) Tsan Visiting Researcher from National Taiwan University of Science and Tech. Center for Intelligent Maintenance Systems (IMS) University of Cincinnati PO Box 210072, Cincinnati, OH 45221 Email: [email protected]

Abstract: The retrieval of product information at every stage of the product lifecycle is essential. This paper investigates and develops a systematic methodology for product lifecycle knowledge management based on embedded informatics that utilizes product maintenance data across the lifecycle of the product. The paper presents a methodology, tools, and two case studies. Emphasis is made on a major gap and dissociation that exist between the product designer and the customer and how product lifecycle management through embedded Infotronics may remedy this situation and allow for product knowledge management through a close-loop approach.

Keywords: product lifecycle; Infotronics; embedded systems; knowledge management; closed-loop lifecycle; maintenance.

Copyright © 2009 Inderscience Enterprises Ltd.

J. Lee, M. AbuAli, C. Deng, C. Tsan

Reference to this paper should be made as follows: Lee, J., AbuAli, M., Deng, C., Tsan, C. (2009) ‘Product Lifecycle Management Using Embedded Infotronics: Methodology, Tools, and Case Studies’, Int. J. Knowledge Engineering And Data Mining, Vol. 1, No. 1.

Biographical Notes: Dr. Jay Lee is Ohio Eminent Scholar and L.W. Scott Alter Chair Professor in Advanced Manufacturing at the Univ. of Cincinnati and is founding director of National Science Foundation (NSF) Industry/University Cooperative Research Center (I/UCRC) on Intelligent Maintenance Systems (IMS www.imscenter.net) which is a multi-campus NSF Center of Excellence between the Univ. of Cincinnati (lead institution), the Univ. of Michigan, and the Univ. of MissouriRolla in partnerships with over 45 global companies including P&G, Toyota, GE Aviation, Boeing, AMD, Caterpillar, Siemens, DaimlerChrysler, ETAS, Festo, Harley-Davidson, Honeywell, ITRI (Taiwan), Komatsu (Japan), Omron (Japan), Samsung (Korea), Toshiba (Japan), Bosch, Parker Hannifin, BorgWarner, Spirit AeroSystems, Nissan (Japan), Syncrude (Canada), and McKinsey & Company, CISCO, TARDEC, etc. His current research focuses on autonomic computing, embedded IT and smart prognostics technologies, design of smart selfmaintenance machines & systems, and dominant design tools for product and service innovation. Mr. Mohamed AbuAli is a Project Leader and Ph.D. Doctoral Researcher at the Center for Intelligent Maintenance Systems (IMS) at the University of Cincinnati, Ohio. He is currently leading many projects with Omron, Nissan, Toyota, and Syncrude. His field of research is in the design and implementation of a systematic methodology for successful predictive maintenance and smart service business that utilized product maintenance data for product lifecycle knowledge management. He has a B.Sc. in Systems Engineering from the University of Arizona and a M.Sc. in Industrial Engineering from the American University in Cairo. He is also a Project Management Professional, a Six Sigma Green Belt, and a Certified Quality Engineer. Ms. Chiao-Lin (Rene) Deng and Ms. Chia-Hsi (Janet) Tsan are international visiting researchers at the Center for Intelligent Maintenance Systems. They are pursuing their Masters (M.Sc.) in Industrial Management at the National Taiwan University of Science and Technology (NTUST). Ms. Deng’s research interest is in supply chain management. Ms. Tsan’s research interest is in inventory management.

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Product Lifecycle Knowledge Management Using Embedded Infotronics

1. Introduction In today’s manufacturing environment, the traditional approach to the management of the lifecycle of a product is material-centric in nature. Raw materials are reduced through a manufacturing process into a usable form that resembles a product with functionality and usability. Data is only collected throughout the product lifecycle until the product is released to the customer. Most often, there is no retrieval of product lifecycle data after the product is sold to the customer. New thinking is needed to transform the material-centric manufacturing environment to an information-centric paradigm. There are three major players with key roles in a product lifecycle: the designer, manufacturer, and end-user or customer (Figure 1). The links between the designer and the manufacture is through supply chain management. Connectivity is also established between the manufacturer and the customer through logistics and customer relationship management. However, the link between the designer and the end-user is non-existing due to the lack of data collection after the product release or sales. Figure 1: Unmet Needs and Gaps in Product Lifecycle Management Designer

End User (Customer)

Logistics Customer Relationship Management

Manufacturer (Product)

The retrieval of product information at every stage of the product lifecycle is essential. This paper investigates and develops a systematic methodology for product lifecycle knowledge management based on embedded informatics that utilizes product maintenance data across the lifecycle of the product. The transformation of product data to useful information is made possible through informatics hardware and software tools that capture this useful product information from the customer and provides a feedback mechanism to the designer, thus creating a close-look product lifecycle design.


2. Product Lifecycle Management (PLM) 2.1 Defining Product Lifecycle Management Product Lifecycle Management (PLM) is the process that governs and manages the entire lifecycle of a product, involving: design, materials acquisition, manufacture, distribution, use, service, maintenance and disposal (Moore et. al, 2007). PLM has become an essential blend of technological and organizational approaches for the effective management of product development in today’s engineering manufacturing industry. PLM is a wide functional totality, a consistent set of systematic methods, models and IT tools that attempts to control the product information. It is not only based on the individual strategy and business architecture of each company, but a globally distributed, interdisciplinary collaboration between producers, suppliers, partners and customers (Miller, 2003). PLM impacts all aspects of the organization. Current traditional PLM approaches rely on database management systems that integrate all available data in a central location and manage process metamodels. PLM systems typically share many common features (Saaksvuori and Immonen, 2005): • Data Vault: a centralized data system for data acquisition. In other words, it constitutes a data warehouse that meets certain set demands. A typical data vault consists of data records acquired at various life cycle stages. • Metadata Base: a set of modules that are needed to maintain the structure of the whole system. The task of the metadata base is to handle relationships between individual pieces of product data, the structure of the information, and the principles needed to ensure the systematic recording of the information. It keeps a record of the data produced by the different systems and applications. • Application: a platform that achieves the PLM functions of information and metadata base management and provides a variety of different user interfaces. The PLM application usually acts as a link between different authoring applications and integrates enterprise software such as ERP. In recent years, PLM approaches have started to integrate development and service partners with customers. Customers can provide their satisfaction experience as feedback during product use. PLM models and methods can integrate the customer feedback into the product development processes (Fathi and Abramovici et al, 2007). This can provide valuable services such as remote monitoring services, remote diagnostic services and more precisely maintenance. Furthermore, it can open new horizons for product design and contributes to the wider exploration and development of next generation product (Moore et. al, 2007). The vision of gathering real-time data on the condition and performance of a product during every cycle of product life, the use of the information to predict remaining useful product life, may possibly lead to extensions of the

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overall product life, reduction of maintenance costs through more effective reuse and recycling and ultimately a vast improvement in the design of future products. This is called close-loop product lifecycle management.

2.2 Modelling Product Lifecycle Management To further aid in understanding PLM approaches, several models have been proposed in the literature. Three different models are briefly reviewed here. A. Manufacturer-User PLM Model Figure 2: PLM Model [A] (Stark, 2005)

As seen by the Manufacturer Imagine

Define

Realize

Define

Retire

(Service)

As seen by the User Imagine

Support

Realize

Use

Dispose

(Operate)

(Recycle)

The concept of products having distinct lifecycles has existed for a long time in many industries. However, the definition of a “lifecycle” is not the same when viewed by users and manufacturers. Both parties have different views on the concept of product life and product lifecycle (see Figure 2). In this model, the manufacturer views the product lifecycle as five distinct phases: imagination, definition, realization, support, and retirement. From the users’ standpoint, the product lifecycle may not start until they actually purchase and start using the product. The lifecycle will then end when the user stops using it or disposes it. The manufacturer and the user only share the first three phases of the lifecycle, while the last two phases are different. When the user is using the product, the manufacturer needs to provide some service to support it. Later in the lifecycle, the manufacturer may stop producing that product and reduce support service. Finally, the manufacturer will no longer provide any support for that product line. During the last phase of the lifecycle, users can dispose the product before the manufacturer retires the product, or continue usage it although the manufacturer may not provide any support service. (Stark, 2005)


B. Information-based PLM Model Figure 3: PLM Model [B] (Grieves, 2006)

Figure 3 shows an information-based PLM model; where at the centre of the model an information core represents all product lifecycle data. This information core does not belong to any one functional area, but is available to all functional areas. Around the information core are functional areas that comprise a product’s lifecycle. There are planning, designing, building, supporting, and disposing (Grieves, 2006). • Plan: the model starts with requirement analysis and planning based on the customer, company, and market. • Design: the requirements mentioned above are taken up by concept engineering and prototyping. Designers must make sure the concepts and prototypes meet all the requirements. During the next step in the cycle, product engineering takes the designs and prototypes and turns them into exact specifications. Product engineering fully specifies the product at this stage. The main tool used for design stage is Computer-aided design (CAD). • Build: after the product is fully specified, it is the role of manufacturing engineering to determine how the product must be built. The manufacturing and production stage is where the product is then physical built. The manufacturing and production staff utilizes the production plan and Bill of Materials (BOM) through a Material and Resources Planning (MRP) process to build the parts and products using optimized resources. Computer-aided manufacturing (CAM) or Computer Integrated Manufacturing (CIM) are two fundamental tools used here. • Support: the sales and distribution functions use product lifecycle data to: (1) determine the functions and specifications of the product, and (2) keep the product performing as expected. This stage provides customers and service engineers with support information for repair and maintenance. • Dispose: the final aspect of the product’s lifecycle is disposal and recycling.

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3. Role of the Maintenance Function in PLM Traditionally, both research and industrial focus was on product design and manufacturing, while not much attention is given to the status of a product after it is sold to the customers. Thus, this may indicate that product maintenance data and recycle information is ignored in the product end-of-life phase (Jumyung et al., 2008). Nowadays, with escalating concerns of environment, finance, and resources, the focus has switched to product lifecycle management for product end-of life assessment and evaluation. The target of manufacturing is not only to produce products efficiently, but also to manufacture a product that could satisfy a customer’s need while minimizing material and energy consumption. Therefore, not only manufacturing data, but also data on product maintenance and recycling, all play essential roles in managing the product lifecycle (Takata S. et al., 2004). The objective of product maintenance is to preserve a product’s functionality and usability so as satisfy customer’s needs and requirements. To integrate the maintenance function with PLM, maintenance action must be systematically planned and implemented during a product’s condition deterioration and upgrade product’s function in order to match the changing needs of customers or of society. Figure 4 represents these different maintenance activities over time. Figure 4: Maintenance Function (Takata S et al., 2004)

The main functions of maintenance include: 1. Maintainability Design: evaluating maintainability in the product development stage to improve design and providing design data for control the maintenance schedule and planning maintenance strategy. 2. Maintenance Strategy Planning: choosing a propriety strategy for each part of product. 3. Maintenance Task Control: based on the strategy to planning and executing task. 4. Evaluation of Maintenance Results: evaluating and validating whether the chosen strategies are suitable or not. 5. Improvement of Maintenance and Product-based Maintenance 6. Dismantling Planning and Execution: at the end of the product life cycle planning and execution disassembly (Takata S. et al., 2004).


Obviously, the role of maintenance has already changed. In the past, “maintenance” reflected a negative image and being regarded as a troublesome function that does generate profit. The perspective of changed role manufacturing changes the manufacturing company’s business model from producing a product to providing a service, where maintenance being a major service (Keller, 2003). Figure 5: Integrating Maintenance over the Lifecycle

As seen in Figure 5, there are three closed loops for maintenance management. The first loop controls the routine maintenance task. The second loop includes maintenance strategy planning that can be improved by getting the real maintenance data from the first loop. The third loop connects the operation and development phases, using maintenance information to improve the product design during its life cycle continuously. The three loops provide a perfect mechanism to help improving product’s function in order to satisfy different operation conditions and environment requirements (Takata S. et al., 2004). Therefore, maintenance information is a foundation for product life cycle knowledge management. How to transform maintenance data to useful information for the product design phase is an important issue for product lifecycle management.

4. Methodology and Tools for Closed-Loop PLM 4.1 Centre for Intelligent Maintenance Systems (IMS) The key issues of the current manufacturing maintenance practices can be defined as: (1) equipment intelligence through self-maintenance – to be able to extract machine and process health and eventually predict machine performance degradation; (2) synchronization intelligence – refers to the seamless communication between the production floor and management unit so that timely and informed decisions can be made based on machine and process health information; and (3) operations intelligence – the ability to leverage on self-

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maintenance and intelligent synchronization to efficiently schedule production and maintenance to achieve a near-zero downtime manufacturing operations. The Center for Intelligent Maintenance Systems (IMS) has been in the pursuit to address these key issues by performing relevant research in order to develop the enabling technologies for the different aspects of self-maintenance, intelligent synchronization and intelligent operations. Figure 6 summarizes the vision of the IMS Center while providing a directed plan on how to solve these maintenance opportunities (Lee, J. et al, 2003). Figure 6 – IMS Centre Vision of Closed-Loop Lifecycle Maintenance Design

From the IMS vision, degradation information from the critical assets can be used to predict their performance to enable near-zero downtime performance. The asset degradation data can also be fed back to the design stage of the equipment for assessing its reusability and predicting its useful life after disassembly and reuse. To address the key issues mentioned earlier, the IMS Centre has recognized that the following technologies need to be developed: • Processing system that extracts machine data, and convert this voluminous data space into simple but comprehensive health information. • Communication infrastructure that can automatically relay health information to appropriate business functions within the company. • Decision-making system that uses the health information to make informed business decisions on the company assets.

4.2 Methodology for Closed-Loop Product Lifecycle Management


Addressing future maintenance services necessitates a systematic “5S” approach. This approach was devised by IMS Center in order to develop and research all aspects of future maintenance infrastructures (Lee, J. et al, 2006). This systematic approach consists of five key elements: •

Streamline: this encompasses techniques for sorting, prioritizing, and classifying data into more feature-based health clusters. This may also include reducing large data sets (from both maintenance history and on-line data DAQ) to smaller dimensions, leading to correlation of the relevant data to feature maps for better data representation.

•

Smart Process: using the right Watchdog Agent® tool for the right application. This requires techniques for selecting appropriate prognostics tools based on application conditions, criticality of each conditions for machine health, and system requirements.

•

Synchronize: converting component data to component degradation information at the local level and further predicting trends of health using a visualized radar chart for decision-ready information. Maintenance data is transformed to health information and to an automated action (i.e. order parts, schedule maintenance based on criticality of component or machine). This process ensures a key characteristic of “Only Handle Information Once” (OHIO).

•

Standardize: creation of a standardized information structure for equipment condition data and health information so that it is compatible with higherlevel business systems and enables the information to be embedded in business ERP and asset management systems. The goal here is to keep the process as a standard approach for day-to-day practices.

•

Sustain: utilizing the transformed data for information-level decision making. System information is then shared amongst all stages of product and business life cycle systems: product design, manufacturing, maintenance, service logistics, and others in order to realize a closed-loop product life-cycle design and continuous improvement of product-level quality and businesslevel performance.

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Product Lifecycle Knowledge Management Using Embedded Infotronics Figure 7 – Methodology for Close-Loop Product Lifecycle Knowledge Management

4.3 Product Lifecycle Management Informatics Toolbox The IMS Center has been spearheading the development of a processing system called the Watchdog Agent® that relies on informatics tools that comprise of hardware and software tools. Simply put, the Watchdog Agent® is an enabling technology that shall allow for a successful implementation of intelligent maintenance. The toolbox is a collection of algorithms that can be used to assess and predict the performance of a process or equipment based on input from sensors, historical data and operating conditions. Referring to Figure 8, the algorithms can be classified into four major categories: signal processing and feature extraction, quantitative health assessment, performance prediction and condition health diagnosis. The historical behavior of process signatures can be utilized to predict future behavior and thus enable forecasting of the process or machine’s performance. Proactive maintenance can therefore be facilitated through the prediction of potential failures before they occur. Figure 8 –Informatics Toolbox for Maintenance-based Knowledge Management


4.4 Product Lifecycle Health Visualization Tools In manufacturing systems, decisions need to be made at different levels; the component level, machine level and system level, as shown in Figure X. Visualization tools for decision making at different levels can be designed to present prognostics information. The functionalities of the four types of visualization tools are described as follows: • Confidence Value for Performance Degradation Monitoring – If the confidence value (0-unacceptable, 1-normal, between 0~1-degradation) of a component drops to a low level, a maintenance practitioner can track the historical confidence value curve to find the degradation trend. The confidence value curve shows the historical/current/predicted confidence value of the equipment. An alarm will be triggered when the confidence value drops under a preset unacceptable threshold. • Radar Chart for Components Health Monitoring – A maintenance practitioner can look at this chart to get an overview of the health of different components. • Health Map for Pattern Classification – A Health Map is used to determine the root causes of degradation or failure. This map displays different failure modes of the monitored components by presenting different failure modes in clusters, each indicated by a different colour. • Risk Radar Chart to Prioritize Maintenance Decision - A Risk Radar Chart is a visualization tool for plant-level maintenance information management that displays risk values, indicating equipment maintenance priorities. The risk value of a machine (determined by the product of the degradation rate and the value of the corresponding cost function) indicates how important the machine is to the maintenance process. The higher the risk value, the higher the priority given to that piece of equipment for requiring maintenance. Figure 9 – IMS Product Lifecycle Visualization Toolbox

(a) Health Chart

(b) Radar Chart

(c) Health Map

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(d) Risk Chart


5. Case Studies 5.1 Engine Lifecycle Knowledge Management for Heavy Equipment The objective of this case study is to provide an integrated lifecycle knowledge management software platform that will offer systematic aid for decision making. The system will identify, update, and display automatically in a decreasing order of failure risks, all the engines of the company at customer sites with their corresponding level of degradation, the potential root causes and their remaining life time to the next breakdown. For this purpose, historical data related to a certain number of selected engines representative of all engines were sampled from the company's database in order to demonstrate the applicability of advanced tools developed at the IMS Center. This historical data includes condition-based operating data of engines and also engines replacement data available in a central knowledge database. Figure 10 – System Architecture for Heavy Equipment Case Study ENGINEERING KNOWLEDGE AND DATA ACQUISITION FROM THE SYSTEM

Engineers

Customer Appliance

Signal Processing & Feature Extraction

A

Knowledge Base

B Group Methods

Data Preparing (Filtering Outlier estimation)

Data Handling Based Feature Extraction

Autoregressive Modeling

Pump 2

Health Assessment & Prediction

C

D

Bayesian Belief Network (BBN)

Fuzzy Logic (FL) Based Decision Making

E

Match Matrix & ARMA Modeling

Pump 1 Cylinder 6 Cylinder 5

Cylinder 4

F Human Machine Interface

AUTOMATIC AID TO DIAGNOSIS DECISION MAKING

AUTOMATIC AID TO PROGNOSIS DECISION MAKING

1 0.8 0.6 0.4 0.2 0

…

…

Remaining Lifetime Prediction Comparison and Predictive Maintenance Planning

(Adapted from IMS Member Company Project)

Figure 10 is an illustration of the integrated system. Three major tools were utilized as follows: • •

ARMA Model - suitable for estimating missing data during the pre-study, and for prediction the evolution of the decision variables. Bayesian Belief Network - used to evaluate and predict the occurrence of a failure event based on information provided by sensors and features extracted from ARMA model prediction.


•

Fuzzy Logic - remaining life prediction using appropriate membership functions obtained based on expertise of engineers. This solution doesn’t require engines mathematical model and enables to implement very quickly a decision making module based on short term history data.

a) Data Acquisition A remote monitoring system for heavy equipment is used to observe and register events and signals from each engine via satellite communication or through the internet. The signals are collected by an on-board computer, from sensors installed on the engine, transmission, and the pump of each machine. This data is critical for the company to support their customers and for deciding several product lifecycle activities including repair and maintenance scheduling. This will have a positive impact on their aftermarket services' quality if the company is able to predict the types failure that can happen on each machine, or able to estimate the remaining life of the main components. The data gathered in can be classified into four categories: • • • •

Equipment characteristics identification data Operating data (Engine on/off & physical variables) Event data (Failure & Maintenance history) Environmental and condition data (location and payload history)

b) Data Streamlining In order to reduce errors in decision making, removing outliers from the raw data is critical. The remaining data then becomes more suitable for feature extraction. Trends of decision variables can then be predicted, by using the ARMA or other appropriate models. There are many methods capable of eliminating outliers. In this case study, the Huber algorithm was used. The algorithm based on the Huber's method for removing the outliers can be summarized as follow: • • •

Step 1 Step 2 Step 3

•

Step 4

•

Step 5

•

Step 6

Calculate the median of all samples Estimate the standard deviation using the above median Decide upper and lower limits with mean value of all samples and the above estimated standard deviation Replace samples with upper or lower value if they are over upper limit or under lower limit Re-calculate mean value of all samples and re-decide upper and lower limits Continue Step 4 and 5 until error between new mean and previous one become small enough

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c) Data Smart Processing The ARMA model (AutoRegressive Moving Average) is based on a statistical method of observation or analysis of time series. It uses passed data and current data, for predicting the next data from a recursive formulation. It is also a parametric model, in which parameter identified through the time could reveal degradation of the original process. p

q

k =1

k =1

yn = å ak yn-k + e n +å ck e n-k where y* are observation of an object and e * are inputs into a system. Missing data due to elimination of outliers or due to simple loss will be estimated using ARMA model. The same model will be used to estimate the trend in decision variable. The concrete procedure for estimation of missing data is as follows: • • • •

Step 1 Step 2 Step 3 Step 4

Decide a dimension of a model (start from low dimension) Select segment of data to model using decided dimension Evaluate an obtained model using the whole of data Retry changing a dimension of a model if the result of evaluation is not desirable

Next, ARMA can be used to find degradation patterns through correlations between some variables time series data of engines that could be used for automatic failure prediction. Such correlations will determine which variables may be used as features and these features can be extracted and used for health assessment. At the current stage, it is important to supplement the decision making with tools such as Bayesian Belief Network, for estimating the risks of failure and the responsible component, in order to take the appropriate decision, and fuzzy logic that enables to reinforce the decisions et to estimate the remaining operation life time, on the basis of engineers knowledge modelled in form of decision rules. d) Information Synchronization Health information for all engines are processed locally on-board and sent back to the design centre. For visualization of results, a monitoring tool is developed (see Figure 11) that depicts a dynamic table interacting with the design centre knowledge base through the remote monitoring tools. For example, by clicking on a machine or an engine or on a component, more detailed diagrams showing CV (Confidence Values) or OEE values (Overall Equipment Effectiveness) could be displayed to help engineers’ decisions. Detailed information could be also displayed on engines regarding the appropriate maintenance action, scheduling and support for instance. Such synchronization of information offers a closed-loop lifecycle design using product maintenance information for engines.

J. Lee, M. AbuAli, C. Deng, C. Tsan BBN & FL Based FMERA Figure 11 –DYNAMIC Information Visualization and Synchronization System ref.

Engine ref.

Failure Mode

Causes

Machines Machine A Engine AX FM 1

Wear Cracking FM2 Cable disc. Sensor fails Injector Machine B Transmis BY Déraillement Wear du câble Cracking Sensor fails D: Detectability C: cost P: Probability S: Severity

Effect on the Component system

D

P

Chute de la cabine

4

0.9

2 H Change engine

0.6

1 M Change Bearing

Chute de la cabine (la chute d'une

Cylinder Inlet Valve R Inlet Valve L Injector … Shaft Gears Bearing

C

BBN output

Pump 2 Pump 1 Cylinder 6

S

Prognostic

Decison Aid

1 0.8 0.6 0.4 0.2 0

Cylinder 5

Cylinder 4

…

…

This case study enabled the implementation of the total chain of IMS’s remote monitoring system in a real world application. It will enable the company to achieve the near-zero downtime for its products and engines and offer an initiative for closed-loop lifecycle management based on product maintenance information. The results obtained from this case study as well as the systematic methodology utilized to acquire them, can be extended from engines to other components such as transmission systems. 5.2 Embedded Product Lifecycle Unit for Knowledge Management In this case study, a methodology for the construction of embedded platforms for product condition assessment and product performance prediction based on the both developments in prognostic algorithm (Watchdog Toolbox, Center of IMS) and the assessment of standard components for extended utilization (Life Cycle Unit (LCU), TU-Berlin) has been developed. Development tasks and building blocks for the product life cycle unit (LCU) are shown in Figure 12. Roller bearings are widely used in rotary machines because of their carrying capacity and low-friction characteristics. Inference about the current condition of a roller bearing can be made based on the vibration signature produced by that bearing. Current literature tries to extract significant features out of the vibration measurements and to relate them to characteristics of a failure mode. Characteristic defect frequencies dependant on the fault location, bearing geometry and rotational speed of the bearing are the key features that researchers pursue in noisy vibration measurements in order to detect and diagnose bearing

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faults. However, a train constantly undergoes unpredictable acceleration and deceleration during its operation. Therefore, most of the time, no constant rotational speed can be defined, which renders state of the art methods not applicable. Figure 12 – System Architecture for Lifecycle Unit Case Study

(Adapted from Research Work with TU-Berlin, Germany)

In this project a method to extract characteristic features for a roller bearing in a railroad bogie that is operating in non-stationary regimes in the sense that its rotational speed is varying constantly over time is presented. Time-frequency analysis was utilized to extract the characteristic features from the vibration signature. The Support Vector Machine (SVM) utilizing the Radial Basic Function (RBF) kernel was used for classifying the current condition of the bearing. Several test runs with varying speeds and loads have been run to simulate non-stationary regimes on a specially developed testbed for freight car roller bearings. Signals of three one-directional vibration sensors, one rpm sensor, two force sensors and one temperature sensor were processed with the above mentioned Watchdog algorithms. Results indicate a high accuracy rate for distinguishing between a functional and a defective bearing, in spite of the nonstationary operating regime. To realize a mobile embedded system for condition monitoring of roller bearings a EWA/LCU prototype based on the Motorola ARM9 system with the pluggable NCAP sensor interface was build. The software architecture was set up according to the model described in Error! Reference source not found..


The EWA/LCU Software Interface and the EWA/LCU Main Application together with the above selected Watchdog algorithms were programmed on a PC and downloaded onto the EWA/LCU prototype. Bluetooth NCAP and STIM modules were used for data transmission of the rpm, force and temperature sensor. The vibration sensors were directly connected to the A/D-converter of the EWA/LCU prototype. For energy supply of the sensors and EWA/LCU prototype a solar panel that charges a battery was used. The bogie testbed has been developed to examine the behavior of freight train cylinder roller bearings (Figure 13). It consists of a bearing housing from the Y25 bogie and one half of an axle powered by an electric motor. The Y25 is a very commonly used bogie in Europe. Figure 13: Bearing Testbed for the Y25 Bogie - Measured Magnitudes

In order to reduce Life Cycle Costs, to improve quality of service and to reduce environmental impact, train manufactures are thinking of new approaches to improve their efficiency. Maintenance and related costs have a significant impact in the railroad industry because the product’s life cycle is relatively long and the components underlie constant wear and deterioration (Figure 14). Figure 14: Life Cycle Costs and Reliability

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To improve efficiency and to facilitate condition based maintenance a EWA/LCU prototype for monitoring of the braking system of a freight car bogie was developed. Its functionality was extended to monitor the pneumatic brake set of a freight car bogie. The EWA/LCU was designed in a way that it can assess the condition of other selected bogie components too, i.e. bearings.

6. Conclusion and Future Work This research has introduced the novel concept of close-loop product lifecycle knowledge management. Two case studies were presented that capture this concept. In summary, a major gap and dissociation exist between the product designer and the customer. The concept of close-loop product knowledge management based on maintenance information acquired through embedded informatics is an innovative solution that can bridge this gap and link the designer to the customer. Such a gap can only be bridged by the successful and precise transformation of data to information and the utilization of this output information for product design improvement in a close-loop systematic methodology. Future research directions include the development of tools that enable the modelling of information into knowledge. Several research tools can be incorporated for knowledge modelling (see Figure 15). Science-based knowledge modelling is essential for the modelling, prediction, and customization of products to fit customer needs. This must be done through the currently missing link between the designer and the user. Figure 15: Future Research Directions in Knowledge Management Current

IMS

Future

Data

Information I

Knowledge

Research Tools

Bayesian Belief Networks Self-Organizing Maps

Information Level

Graph Theory Risk Modeling Human Factors Analysis Value Chain Modeling

Knowledge Level


7. References Fathi, M., Holland, A., Abramovici, M., Neubach, M., “Advanced condition monitoring services in product lifecycle management”, Institute of Electrical and Electronics Engineers, 2007. Grieves, M, “Product Lifecycle Management- Driving the Next Generation of Lean Thinking”, McGraw-Hill, 2006. Keller, E: Customer Support, “Delivering Enhanced Services through Intelligent Device Management, Service Business Magazine”, 2003: Nov/Dec. Lee, Jay, et al. “Watchdog Agent: Infotronics-based Prognostics Approach for Product Performance Degradation Assessment and Prediction.” Advanced Engineering Informatics (2003) Lee, Jay, et al. "Intelligent Prognostics Tools and E-Maintenance." Computers in Industry (2006): 476-489. Moore, P.R, Pu, J., Wong, C.B., Chong, S.K., Yang, X., “Product lifecycle information acquisition and management for consumer products”, Industrial Management & Data System, 2007, 107(7), 936-953. Saaksvuori, A., Immonen, A., “Product Lifecycle Management”, Springer, 2008. Stark, J, “Product Lifecycle Management-21st Century paradigm for product realization”, Springer, 2005. Takata, S.; Kirnura, F.; van Houten, F.J.A.M.; Westkamper, E.; Shpitalni, M.; Ceglarek, D.; Lee, J., “Maintenance: Changing Role in Life Cycle Management”, CIRP Annals - Manufacturing Technology, 2004, 53(2), 643655. Um, J, Yoon, J.S., Suh, S.H., “An architecture design with data model for product recovery management systems”, Resources, Conservation and Recycling, 2008, 52, 1175-1184.

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