Diagnosis model for industrial equipment fault diagnosis, construction method thereof and application thereof

Abstract

The invention discloses a diagnosis model for industrial equipment fault diagnosis, a construction method thereof and an application thereof. The construction method comprises the steps: employing a variational mode decomposition method to decompose and train each original vibration signal of industrial equipment, obtaining a plurality of sub-modal components, and selecting an optimal sub-modal component from the plurality of sub-modal components; adopting a Bayesian optimization one-dimensional fast non-local mean method to denoise all the optimal sub-modal components; and based on all the denoised optimal sub-modal components, adopting metric learning to change a sample distance metric function in the classifier and training to obtain a diagnosis model of industrial equipment fault diagnosis. According to the construction method, variational mode decomposition is adopted to separate fault features from original vibration signals; and a non-local mean denoising algorithm with high denoising performance is further introduced, and Bayesian optimization is carried out on the parameters, so that a good denoising effect on the vibration signal with the high signal-to-noise ratio is realized; and finally, metric learning is applied to training of the classifier. The construction method is high in diagnosis accuracy and wide in application range.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of industrial equipment, and more particularly relates to a diagnostic model for fault diagnosis of industrial equipment and its construction method and application. Background technique [0002] As an important component of rotating machinery and equipment, rolling bearings are widely used in various industrial equipment. Bearings often work in a high-load and high-speed environment, and their life shows obvious discreteness and unpredictability. More than 90% of the bearings' actual life is less than the design life. Once a bearing fails, it can easily lead to equipment downtime and even pose a threat to personal safety. It is of great significance both in theory and in reality to study a set of effective methods to realize the fault diagnosis of bearings. [0003] The vibration signal acquisition of rolling bearings is relatively simple and contains rich information. Therefore, the analysis bas...

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Problems solved by technology

[0006] The invention provides a diagnostic model for fault diagnosis of industrial equipment and its construction method and application, which are used to solve the problem of insufficient denoising of the original vibration signal and the failure of the fault diagnosis model to input data in the diagnostic model construction method of the existing industrial equipment fault diagnosis. Dimensions, dimensions and other generalizations are not strong enough to lead to technical problems that are not practical enough in the actual industrial equipment fault diagnosis

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