A multivariate-based equipment dynamic health state assessment method

Abstract

The invention discloses a multivariable-based equipment dynamic health state assessment method. A multivariable model combining the comprehensive significance-bias convolutional neural network and thedecreasing maximum entropy fuzzy clustering is utilized to perform equipment dynamic health state evaluation; the method has the advantages that the training time is shortened, the model precision and generalization ability are improved, the fault threshold can be dynamically distributed according to the environment, the real working condition of the equipment is met, and the accurate health state evaluation and life prediction of the equipment are realized.

Description

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AI Technical Summary

Benefits of technology

The technical effect that this invention focuses on evaluating equipment's performance based upon their current environmental status during operation. It suggests assigning specific levels or ranges of values called critical points at certain times when there are no significant problems with them. These critical points help predict how well these devices will last over its lifetime without being damaged by any factors like vibration or impact from external influences such as rainwater. By doing so, they improve maintenance efficiency while reducing costs associated with replacing worn out components.

Problems solved by technology

Technological Problem: Current solutions involve manual inspections and fail predictions made after significant periods of non-operational downgrades over their lifespan. These techniques can be expensive and prone to errors caused by subjectivity and poor judgment. Additionally, these approaches only provide static estimates without accounting for future changes such as deteriorating components undergoing agitation within an operational period.

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