Abstract
This paper presents a multi-state adaptive early warning method for mechanical equipment and proposes an adaptive dynamic update model of the equipment alarm threshold based on a similar proportion and state probability model. Based on the similarity of historical equipment, the initial thresholds of different health states of equipment can be determined. The equipment status is divided into four categories and analyzed, which can better represent its status and provide more detailed and reasonable guidance. The obtained dynamic alarm lines at all levels can regulate the operation range of equipment in the different health states. Compared to the traditional method of a fixed threshold, this method can effectively reduce the number of false alarms and attains a higher prediction accuracy, which demonstrates its effectiveness and superiority. Finally, the method was verified by means of lifetime data of a rolling bearings. The results show that the model improves the timely detection of the abnormal state of the equipment, greatly reduces the false alarm rate, and even overcomes the limitation of independence between the fixed threshold method and equipment state. Moreover, multi-state division can accurately diagnose the current equipment state, which should be considered in maintenance decision-making.
Highlights
With the rapid development of high-tech, modern machinery and equipment are becoming more complex in structure and abundant in functions
The operating status of key components is directly related to the performance of machinery and equipment, so the failure in the timely detection of abnormalities of components may cause the malfunction of the entire system [1]
In the actual production process, the probability density function of the collected data is often unknown, and the specific distribution form cannot be determined, so the kernel density estimation method is adopted to analyze the distribution law that is not known in advance
Summary
With the rapid development of high-tech, modern machinery and equipment are becoming more complex in structure and abundant in functions. Zhang et al [26] applied the improved multi-state estimation technology of the process memory matrix to provide an early warning of failure of auxiliary equipment in power plants. These methods study the setting of the warning value from the perspective of the algorithm, they are still essentially dichotomy-based methods with a fixed threshold and still possess the above disadvantages. In addition to identifying whether the equipment is operating abnormally, its state needs to be determined, and different dynamic warning values should be set for its different states.
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