Mechanical wearing part performance assessment and prediction method based on EMD (empirical mode decomposition)-SVD (singular value decomposition) and MTS (Mahalanobis-Taguchi system)

Abstract

The invention provides a mechanical wearing part performance assessment and prediction method based on based on EMD (empirical mode decomposition)-SVD (singular value decomposition) and an MTS (Mahalanobis-Taguchi system), and belongs to the technical field of mechanical wearing part fault diagnosis. The method comprises: first of all, performing noise reduction processing on acquired signals of a monitored object, then performing EMD on the signals, selecting effective IMF (intrinsic mode function) components and residual functions to form an initial matrix, performing SVD on the initial matrix, and performing normalization processing on obtained characteristic values to obtain characteristic vectors; then using an MTS method to calculate an MD (Mahalanobis Distance), and using a Taguchi method to perform optimization and reduction on the characteristic vectors; and converting the MD into a confidence value, performing assessment on the performance of mechanical wearing parts through tracking the trend of the confidence value, and performing prediction on a fault through a correlation module or a matching matrix between the confidence value and conditions of the monitored object. The method provided by the invention avoids the problem of easily occurring errors when a conventional method is used for processing non-linear non-stationary signals, and reduces fault generation probability, thereby being suitable for industrial real-time monitoring.

Description

technical field [0001] The invention relates to a technology for performance evaluation and prediction of mechanical wear parts, and belongs to the technical field of fault diagnosis of mechanical wear parts. Background technique [0002] With the development of science and technology, modern industry is developing rapidly in the direction of automation, and the popularity of industrial automation is getting higher and higher. Industrial automation not only requires that the production and manufacturing processes such as machines and equipment can run automatically, but also the supporting control process is also essential. The control process is mainly used to monitor the operating status of the equipment to ensure its normal operation, so as to control the quality of the final product. Mechanical wear parts such as cutting tools in CNC lathes, CNC milling machines and other machine tools, as well as commonly used gears, bearings, etc., are key components of various equipm...

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Problems solved by technology

[0010] The purpose of the present invention is to solve the problem of difficult non-stationary nonlinear signal processing and inaccurate health assessment in traditional mechanical wear parts health assessment and prediction methods in the industrial control process, and to solve the existing problems based on neural network, chaos, etc. The method

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