Examination of the reliability of a technical object after its regeneration in a maintenance system with an artificial neural network

This article presents the issues of an examination and evaluation of the reliability of a technical object after its regeneration in a maintenance system. The preventive activities (regeneration) of an object are conducted in a maintenance system which includes an artificial neural network. The author made an attempt in the paper to prove that an artificial neural network has an indirect influence on the quality of preventive activities organized for a technical object and hence on the level of its reliability. This influence is the result of the fact that information developed by a neural network constitutes the basis in the process of the generation of an expert knowledge base on the basis of which the maintenance system is organized. This paper also covers the theoretical basis. Those quantities were defined which may be used in reliability tests of technical objects. The possibilities were presented of testing the reliability: the quality of the use of an object after its regeneration. These tests may be carried out in a short period of time and in a longer period of time after the preventive activities have been carried out for a device. This article also presents the idea of a reliability test conducted for a technical object on the example of a device which controls the work of a petrol engine. For this purpose, the structure of a maintenance system was designed on the basis of an expert knowledge base determined for an object. Diagnostic information from an artificial neural network constitutes the basis for these activities. A substantial part of the article is devoted to the description of the method of the reliability test of the object and to an analysis of the results obtained. The test was carried out in a simulation manner after a long period of time following regeneration of a technical object.

[1]  Boris Vladimirovič Gnedenko,et al.  Metody matematyczne w teorii niezawodności , 1968 .

[2]  Hojjat Adeli,et al.  Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems , 1994 .

[3]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[4]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[5]  Stanisław Duer,et al.  Artificial Neural Network-based Technique for Operation Process Control of a Technical Object , 2009 .

[6]  C. Whitney,et al.  Building "Expert systems" when no experts exist , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[7]  B. Żółtowski,et al.  Determination of diagnostic information of a technical object on the basis of a functional and diagnostic analysis on example of a car engine , 2008 .

[8]  M. Mathirajan,et al.  Heuristic algorithms for scheduling heat-treatment furnaces of steel casting industries , 2007 .

[9]  Stanislaw Duer,et al.  Diagnostic system with an artificial neural network in diagnostics of an analogue technical object , 2010, Neural Computing and Applications.

[10]  Toshio Nakagawa,et al.  Maintenance Theory of Reliability , 2005 .

[11]  William J. Clancey,et al.  Rule-based expert systems , 2017, Radiopaedia.org.

[12]  Lixin Tang,et al.  Modelling and a genetic algorithm solution for the slab stack shuffling problem when implementing steel rolling schedules , 2002 .

[13]  Toshio Nakagawa,et al.  Optimal inspection policies for a storage system with degradation at periodic tests , 2000 .

[14]  Stanislaw Duer,et al.  Diagnostic system for the diagnosis of a reparable technical object, with the use of an artificial neural network of RBF type , 2010, Neural Computing and Applications.

[15]  A. Katunin,et al.  Classification of the elements of the object's maintenance structure of a repairable technical object , 2009 .

[16]  Stanislaw Duer,et al.  Investigation of the operation process of a repairable technical object in an expert servicing system with an artificial neural network , 2010, Neural Computing and Applications.

[17]  Madan M. Gupta,et al.  Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory , 2003 .

[18]  Balbir S. Dhillon,et al.  Applied Reliability and Quality , 2007 .