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El. knyga: Diagnostics of Mechatronic Systems

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This book provides novel approach to the diagnosis of complex technical systems that are widely used in various kinds of transportation, energy, metallurgy, metalworking, fuels, mining, chemical, paper industries, etc.

Effective diagnostic systems are necessary for the early detection of errors in mechatronic systems, for the organization of maintenance and for the assessment of the performed service quality. Unfortunately, the practical use of AI in the diagnosis of mechatronic systems is still quite limited and the inability to build effective mechatronic systems leads to significant economic losses and dangers.  

The main aim of this book is to contribute to knowledge within the topic of diagnostics of mechatronic systems by the analysis of the elements reliability characteristics, using methods, models and algorithms for diagnostics and by studying examples of model diagnostic systems using AI methods based on neural networks, fuzzy inference systems and genetic algorithms. 

1 The Basics Characteristics of Elements Reliability
1(16)
1.1 Core Concepts
1(1)
1.2 Terms and Definitions of Mechatronics and Diagnostics
2(3)
1.3 The Element Reliability Characteristics
5(1)
1.4 The System Reliability Characteristics
5(1)
1.5 The Serial Structure
6(2)
1.6 The Parallel Structure
8(2)
1.7 Serial and Parallel Structure
10(4)
References
14(3)
2 Methods, Models, Algorithms for Diagnostics of Mechatronic Systems
17(10)
2.1 Methods of Mechatronic Systems Diagnostics
17(2)
2.2 Diagnostic Models of Mechatronic Systems
19(2)
2.3 Algorithms for the Mechatronic Systems Diagnostics
21(4)
References
25(2)
3 Model Systems for Diagnosticing of Mechatronic Objects
27(36)
3.1 Models of Information Processes for Diagnostics of Mechatronic Systems
27(7)
3.2 Example of a Neural Network for Bearing Diagnostics
34(6)
3.3 Example of Diagnostic Tools Based on Fuzzy Inference Systems
40(6)
3.4 Example of Diagnostics of Mechatronic Dynamic Modules
46(2)
3.5 Hardware Equipment for Diagnosing Mechatronic Systems
48(7)
3.6 Multicriterial Optimization of Diagnostic Systems
55(4)
3.7 Conclusions
59(1)
References
60(3)
Appendix A Example of a CNC Machine Diagnostics Program 63(12)
Appendix B Source Code of the Fuzzification Program of an Input Variable Using the Gaussian Curve Membership Function 75
Appendix C Source Code of the Conclusions of the Accumulation Program Fuzzy Rules of Production and Output Variable Defuzzification
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