Prognosis Monitoring System: methods and application cases

Transcription

1 Prognosis Monitoring System: methods and application cases Mechanical and Mechatronics Systems Research Laboratories (MMSL) Hung-Tsai Wu

2 Mechanical and Mechatronics Systems Research Laboratories (MMSL) A total of 7 divisions, 26 departments, 506 staffs (Ph.D. 19%, MS 46%) Vehicle homologation Intelligent Mobility Technology Controller Kernel Technology MMSL Industry IoT Technology Advanced Machinery Technology Advanced Manufacturing Technology Intelligent Robotics Technology Copyright 2017 ITRI 工業技術研究院 2

3 About us Prognosis & Decision Tech. Dept. Starting from vibration signal analysis, the Prognosis & Decision Tech. Dept. has been engaged in rotating machinery vibration monitoring, fault diagnosis, modal analysis, and structural analysis for more than 20 years, and has served more than 100 different companies, including machine tools, metal working, plant equipment, nuclear plants, and robotics. Copyright 2017 ITRI 工業技術研究院 3

4 What is Prognosis? The Evolution of Maintenance Strategies Run to failure No maintenance is performed on the asset until the failure event Used where the economical impact of breakdown is none or minimum The most expensive method (including inventory costs) Time-based/Preventative Maintenance Service intervals specficied by OEM/experienced staffs Maintenance is performed whether the equipment needs it or not Does not identify problems that develop between the scheduled inspections Predictive/Condition-Based Maintenance Maintenance is only performed when the machinery needs it The skill level, knowledge and experience required to accurately interpret condition monitoring data can be high Cost saving by minimizing (un)scheduled downtime and thus increasing production Copyright 2017 ITRI 工業技術研究院 4

5 What is Prognosis? (cont.) Q1: there should not be any components in this quadrant because such issues should have been noticed and fixed during the design stage Q2: should have an adequate number of spare parts on hand Q3: the current maintenance practices are working for these components and no changes are required Q4: here lies the most critical components, thus predictive maintenance should be employed *J. Lee et al., Prognostics and health management design for rotary machinery systems Reviews, methodology and applications, Mechanical Systems and Signal Processing, 42 (2014), Copyright 2017 ITRI 工業技術研究院 5

6 Prognosis Monitoring System (PMS) Needs Health condition monitoring of rotating machinery is essential to both makers and users, but the current solution of time-based or preventative maintenance are mostly performed off-line and regardless of needs. Solutions Three core modules/algorithms are developed for predictive maintenance of various types of rotating machinery. The performance assessment module is capable of evaluate the health status. The trend prediction module is used to indicate possible changes of the health status. The fault diagnosis module is constructed according to domain expert knowledge. Benefits By employing the PMS, the users can benefit from on-line health condition monitoring, accurate fault localization, cost saving, and so on. Maintenance is only performed when the machinery needs it, so most of the unscheduled/unwanted downtime can be eliminated. Copyright 2017 ITRI 工業技術研究院 6

7 Prognosis Monitoring System (PMS) (cont.) Performance Assessment Module Using time/freq. domain feature extraction and regression algorithms, and taking into consideration the characteristics of the equipment and corresponding international standards, the performance assessment module can be used to perform on-line health status monitoring. Users can also specify the monitoring items and thresholds, such as the amount of vibration, specific spectral sub-band components, etc.. Copyright 2017 ITRI 工業技術研究院 7

8 Prognosis Monitoring System (PMS) (cont.) Trend Prediction Module For a better prediction accuracy, data smoothing is firstly conducted on the historical data of the performance assessment module, afterwards time-series prediction algorithms are employed for predicting when will the equipment s performance reaches a customized maintenance point. This could help in lengthen the MTTF, and approaching near-zero downtime. Copyright 2017 ITRI 工業技術研究院 8

9 Prognosis Monitoring System (PMS) (cont.) Fault Diagnosis Module Using time/freq. domain feature extraction and fuzzy neural network learning, and taking into consideration the characteristics of the equipment and expert experiences, the fault diagnosis module can be used to identify more than 20 types of faults regarding critical components such as shaft, bearing, gear box, and motor. This could help in avoid misjudgment made by the staffs, and shorten the MTTR. Copyright 2017 ITRI 工業技術研究院 9

10 Application Scenario Copyright 2017 ITRI 工業技術研究院 10

11 Machine Tools (Maker) Use Cases Needs: All the machining centers must be tested with calibration artifactsbefore they can be delivered. Current Solution: Exterior observation of the calibration artifacts by the staffs, or using vibration meter to make go or no-go decisions. Expected Solution: Employ the performance assessment module of PMS for gradingthe healthstatusof the machine. Copyright 2017 ITRI 工業技術研究院 11

12 Machine Tools (Maker) Use Cases (cont.) Needs: Gathering the performance and fault diagnosis data for better after-sales service and for a reference of the development of new machine tools. Current Solution: On site inspection by the staffs. Expected Solution: Employ the fault diagnosismodule of PMS. Copyright 2017 ITRI 工業技術研究院 12

13 Grinders (User) Use Cases (cont.) Needs: Integrate performance monitoring and diagnosis functions for the development of next-generation equipment. Solution: Employ PMS for grinder spindles. Copyright 2017 ITRI 工業技術研究院 13

14 Nuclear Plants Use Cases (cont.) Needs: Vibration monitoring systems for recirculation pump. Solution: Employ PMS for remote monitoring, where the signal is collected and processed in ITRI. The signal condition module was done by ourselves. Copyright 2017 ITRI 工業技術研究院 14

15 Wind Turbine Use Cases (cont.) Needs: Remote vibration monitoring, life cycle prediction, and fault diagnosis. Solution: Employ PMS for remote monitoring. Copyright 2017 ITRI 工業技術研究院 15

16 Copyright 2017 ITRI 工業技術研究院