Similarity measures for diagnostic symptom evolution. Issue 1 (1st February 2016)

• Record Type: Journal Article
• Authors: Tabaszewski, Maciej; Cempel, Czeslaw
• Is Part Of: Grey systems. Volume 6:Issue 1(2016)
• Page Start: 51
• Page End: 63
• Publication Date: 2016-02-01
• Subjects: Condition monitoring -- GRA models -- Grey modelling -- Similarity of diagnostics symptoms; Cybernetics -- Periodicals; Systems engineering -- Periodicals; 003.5
• DOI: 10.1108/GS-10-2015-0072
• Languages: English
• ISSNs: 2043-9377

Abstract

Abstract : Purpose: – The observed diagnostic symptoms are often characterized by local fluctuations of their values. Hence, instead of direct observation of symptoms it is worth observing their grey models and research similarity between life curves, which can enable to guess the nature of wear. The purpose of this paper is to find useful measures of similarity of diagnostics symptoms modeled by GM(1, 1). Design/methodology/approach: – Measures of similarity may be used to determine the character of wear of the diagnosed object by way of comparison with known examples, which have previously been obtained and identified. A methodology for creation of such comparisons based on pre-smoothing by means of a GM(1, 1) model with rolling window has been proposed. The process of smoothing enables to eliminate local fluctuations of a symptom. Their existence makes it difficult to compare symptoms. Application of a rolling window enables in turn to map the symptom properly, which may be difficult in the case of relatively short period of accelerated wear and changes of symptom values. To compare the life curves it is also necessary to normalize the life curves, so that they are represented by the same number of measurements (compression or extension of the measure of operation). Findings: – The paper concerns the similarity measures for symptom life curves obtained during vibration monitoring of fan mills working at a heat and power station. Similarity measures of symptoms were proposed and applied to the acquired data from the machines. Practical implications: – The method of symptom modeling and life curve comparing can be used to discover type of wear of the machine and eventually estimation of the remaining useful life. Originality/value: – The proposed method is very important for development of condition monitoring.