Physical Model-Based Prognostics and Health Monitoring to Enable Predictive Maintenance

This chapter addresses the development and application of predictive maintenance concepts for several types of assets, following two approaches: (1) detection and prediction of failures based on (real-time) monitoring the health or condition of the systems, and (2) prediction of failures (prognostics) using physical failure models and monitoring of loads or usage. Firstly, several challenges in the field of predictive maintenance are presented. These challenges will be addressed by the methods and tools discussed in the remainder of the chapter. Both the structural health monitoring methods and the prognostic concepts presented are based on a thorough understanding of the system and physical failure behaviour. After discussing the approaches for monitoring and prognostics, a series of decision support tools is presented. As a large number of methods and techniques are available, the selection of the most suitable method, as well as the critical parts in a system, is a challenging task. The presented tools assist in this selection process. Finally, the practical implementation of the presented approaches is discussed by showing a number of case studies in different sectors of industry.

[1]  Moamar Sayed-Mouchaweh Learning from Data Streams in Dynamic Environments , 2015 .

[2]  Joseph L. Rose,et al.  Active health monitoring of an aircraft wing with embedded piezoelectric sensor/actuator network: I. Defect detection, localization and growth monitoring , 2007 .

[3]  Berend Jan van der Zwaag,et al.  RoADS: A Road Pavement Monitoring System for Anomaly Detection Using Smart Phones , 2015, MSM/MUSE/SenseML.

[4]  Tiedo Tinga Predictive Maintenance of Military Systems Based on Physical Failure Models , 2013 .

[5]  Zhongqing Su,et al.  Fundamentals and Analysis of Lamb Waves , 2009 .

[6]  M. Brian Blake Monetizing Autonomous Control , 2017, IEEE Internet Comput..

[7]  Gang Niu,et al.  Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network , 2018 .

[8]  Tomas Jendel,et al.  Prediction of wheel profile wear—comparisons with field measurements , 2002 .

[9]  Richard Loendersloot,et al.  Vibration Based Damage Identification in a Composite T-Beam Utilising Low Cost Integrated Actuators and Sensors , 2012 .

[10]  Takehisa Yairi,et al.  A review on the application of deep learning in system health management , 2018, Mechanical Systems and Signal Processing.

[11]  Tiedo Tinga,et al.  Framework for the selection of the optimal preventive maintenance approach , 2018 .

[12]  Tiedo Tinga,et al.  Vibro-acoustic modulation–based damage identification in a composite skin–stiffener structure , 2014 .

[13]  Enrico Zio,et al.  Reliability engineering: Old problems and new challenges , 2009, Reliab. Eng. Syst. Saf..

[14]  K. Johnson,et al.  Three-Dimensional Elastic Bodies in Rolling Contact , 1990 .

[15]  Pizhong Qiao,et al.  Vibration-based Damage Identification Methods: A Review and Comparative Study , 2011 .

[16]  Richard Loendersloot,et al.  Non-collinear wave mixing for non-linear ultrasonic detection of physical ageing in PVC , 2012 .

[17]  T. H. Ooijevaar Vibration based structural health monitoring of composite skin-stiffener structures , 2014 .

[18]  Martin Bach,et al.  Damage Assessment in Composite Structures based on Acousto Ultrasonics – Evaluation of Performance , 2016 .

[19]  X. L. Liu,et al.  Investigation on the Design of Piezoelectric Actuator/Sensor for Damage Detection in Beam with Lamb Waves , 2013 .

[20]  Charles R. Farrar,et al.  The fundamental axioms of structural health monitoring , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[21]  Tiedo Tinga,et al.  Nonlinear dynamic behavior of an impact damaged composite skin–stiffener structure , 2015 .

[22]  Nobuo Oki,et al.  Accuracy and multi domain piezoelectric power harvesting model using VHDL-AMS and SPICE , 2016, 2016 IEEE SENSORS.

[23]  E. Peter Carden,et al.  Vibration Based Condition Monitoring: A Review , 2004 .

[24]  Tiedo Tinga,et al.  Towards Informed Maintenance Decision Making: Guiding the Application of Advanced Maintenance Analyses , 2017 .

[25]  J. Archard Contact and Rubbing of Flat Surfaces , 1953 .

[26]  Paul J. M. Havinga,et al.  Industrial Wireless Monitoring with Energy-Harvesting Devices , 2017, IEEE Internet Computing.

[27]  Tiedo Tinga,et al.  Selecting Suitable Candidates for Predictive Maintenance , 2018 .

[28]  Matin Shahzamanian Sichani On Efficient Modelling of Wheel-Rail Contact in Vehicle Dynamics Simulation , 2016 .

[29]  Tiedo Tinga,et al.  Impact Damage Identification in Composite Skin-Stiffener Structures Based on Modal Curvatures , 2016 .

[30]  Lin Ye,et al.  Guided Lamb waves for identification of damage in composite structures: A review , 2006 .

[31]  Tiedo Tinga,et al.  Rail Wear Estimation for Predictive Maintenance: a strategic approach , 2018 .

[32]  Richard Loendersloot,et al.  Development of an inline water mains inspection tech nology , 2016, 2016 IEEE International Ultrasonics Symposium (IUS).

[33]  Rittwik Jana,et al.  TCP and MP-TCP in 5G mmWave Networks , 2017, IEEE Internet Computing.

[34]  Prabu Duplex Design of a life prediction tool for high-speed diesel engines , 2018 .

[35]  Spilios D. Fassois,et al.  Statistical Time Series Methods for Vibration Based Structural Health Monitoring , 2013 .

[36]  Jens Baaran Visual Inspection of Composite Structures , 2009 .

[37]  Tiedo Tinga,et al.  An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP , 2017 .

[38]  Peter Wierach,et al.  Development of a Door Surround Structure with Integrated Structural Health Monitoring System , 2016 .

[39]  Fatjon Seraj Rolling vibes: continuous transport infrastructure monitoring , 2017 .

[40]  Tiedo Tinga,et al.  Principles of Loads and Failure Mechanisms: Applications in Maintenance, Reliability and Design , 2013 .

[41]  Tiedo Tinga,et al.  Detection of microbiologically influenced corrosion by electrochemical noise transients , 2014 .

[42]  Mark M Derriso,et al.  Industrial Age non-destructive evaluation to Information Age structural health monitoring , 2014 .

[43]  Benjamin Eckstein,et al.  Identification of Barely Visible Impact Damages on a Stiffened Composite Panel with a Probability-based Approach , 2015 .

[44]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[45]  Tiedo Tinga,et al.  Application of physical failure models to enable usage and load based maintenance , 2010, Reliab. Eng. Syst. Saf..

[46]  Richard Loendersloot,et al.  Acousto-Ultrasonic Damage Monitoring in a Thick Composite Beam for Wind Turbine Applications , 2018 .

[47]  M. Wilkinson,et al.  Towards the Zero Maintenance Wind Turbine , 2006, Proceedings of the 41st International Universities Power Engineering Conference.

[48]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[49]  Abderrahim Mouatamir,et al.  Decision Support System for Condition Monitoring Technologies , 2018 .

[50]  George Lampeas,et al.  Damage Identification in Composite Panels—Methodologies and Visualisation , 2016 .

[51]  Richard Loendersloot,et al.  Development of a piezoelectric based energy harvesting system for autonomous wireless sensor nodes , 2014 .

[52]  Nirvana Meratnia,et al.  RoVi: Continuous transport infrastructure monitoring framework for preventive maintenance , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[53]  Tiedo Tinga,et al.  Physics based methodology for wind turbine failure detection, diagnostics & prognostics , 2015 .

[54]  Yishou Wang,et al.  Validation and evaluation of damage identification using probability-based diagnostic imaging on a stiffened composite panel , 2015 .

[55]  Richard Loendersloot,et al.  Damage identification in composite panels using guided waves , 2015 .

[56]  Julián Sierra-Pérez,et al.  Fiber Optics Sensors , 2013 .

[57]  Tiedo Tinga,et al.  A Critical Appraisal of the Interpretation of Electrochemical Noise for Corrosion Studies , 2014 .

[58]  Eugene J. O'Brien,et al.  Extending the Assessment Dynamic Ratio to Railway Bridges , 2014 .

[59]  Mayorkinos Papaelias,et al.  Wind turbine reliability analysis , 2013 .

[60]  Tiedo Tinga,et al.  The Detection of Fatigue Damage Accumulation in a Thick Composite Beam Using Acousto Ultrasonics , 2018 .

[61]  A. M. R. Ribeiro,et al.  A review of vibration-based structural health monitoring with special emphasis on composite materials , 2006 .

[62]  David W. Greve,et al.  Lamb waves and nearly-longitudinal waves in thick plates , 2008, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[63]  Charles R Farrar,et al.  Damage prognosis: the future of structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[64]  Tiedo Tinga,et al.  Novel time–frequency characterization of electrochemical noise data in corrosion studies using Hilbert spectra , 2013 .

[65]  Tiedo Tinga,et al.  Design Framework for Vibration Monitoring Systems for Helicopter Rotor Blade Monitoring Using Wireless Sensor Networks , 2013 .