A Fault Diagnosis Methodology Based on Non-Stationary Monitoring Signals by Extracting Features With Unknown Probability Distribution

Abstract

This paper studies features with the characteristic of unknown probability distribution, and its application on fault diagnosis based on non-stationary monitoring signals, which mainly consider the uncertainty as the main factor in masking fault diagnosis of practical industrial system. Generally, the probability distribution of the signal feature is unknown and prior information of the trend term is lacking. For this reason, different feature extraction methods, such as time-domain, frequency-domain and time-frequency-domain methods, have always been used to extract features, and they can be used to generate a high-dimensional and nonlinear initial feature set. However, the features' probability distribution is still unknown and prior information of the trend term is still lacking. In order to solve this top problem, Restricted Boltzmann Machine (RBM), with the advantage of feature learning and selection for initial feature set, has been stacked layer by layer to realize a high-dimensional nonlinear mapping between non-stationary signal features and fault modes. Two fault diagnosis experiments on self-confirmation sensor and rolling bearing shown the robustness and effectiveness of this proposed method.

A novel performance trend prediction approach using ENBLS with GWO

“…The GWO [43][44][45] algorithm has few parameters, a simple structure, strong search ability and fast convergence speed. It continuously iterates to find the optimal solution by simulating the wolf predation strategy and social hierarchy.…”

Sensor-driven Learning of Time-Dependent Parameters for Prescriptive Analytics

“…Real-world data streams are usually non-stationary in which the underlying distribution evolves and must be analyzed and adapted accordingly in (near) real-time [9], [10], [14]. This aspect is of increasing importance as more and more data is organized in the form of data streams rather than static, and it is unrealistic to expect that data distributions stay stable over a long period [14].…”

“…Examples include decisions about: economic order quantity models, predictive maintenance actions, online advertisements for offers, etc. However, their estimation poses significant challenges due to the uncertainty derived from inaccurate user input, noisy data, and nonstationarity of real-world data streams [9], [10].…”