Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Abstract

Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model's ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

The Use of Multicriteria Inference Method to Identify and Classify Selected Combustion Engine Malfunctions Based on Vehicle Structure Vibrations

“…Usually the knowledge representation language is imposed by the inductor. In a supervised environment, the hF ( L ) hypothesis generated by the inductor in the learning process is used to classify objects [ 9 , 10 ]. The hypothesis uniquely determines a certain partition PF ( U ).…”

“…Many researchers propose the use of advanced detection algorithms in the form of artificial intelligence, including neural networks, to diagnose engine failure in machines. Huang and Liu [ 10 ] proposed a multifeature fusion model based on Dempster–Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The proposed model in comparison with the k-nearest-neighbors method may effectively improve the accuracy of damage forecasting.…”

Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

“…Most of them are qualitative or unsystematic analyses that are based on a specific model for specific environments. A couple of examples are the identification of signals of the future, related to terrorism or mass transport attacks [22] or master planning London's Olympic legacy [23]; sensor-based human activity recognition [24], mechanical fault prediction by sensing vibration [25], or a deep learning analysis to predict parking space availability [26]. Other examples are the influence of maximization in social networks [27], a model to examine what schools would be like if they were a new invention [28], or a deep analysis about charge prediction for criminal cases [29].…”

Removal of Dust Microelectric Signal Based on Empirical Mode Decomposition and Multifractal Detrended Fluctuation Analysis

“…Combining the theory of multifractals and detrended fluctuation analysis, Kantelhardt et al [ 27 ] developed multifractal detrended fluctuation analysis (MFDFA). MFDFA has since been widely applied to stock market analysis [ 28 ], temperature time series [ 29 ], seismic wave signals [ 30 ], vibration tomographic diagnosis [ 31 ], image processing [ 32 ], and voice signal analysis [ 33 ]. MFDFA can effectively describe the nonlinear measurement signal, especially the multifractal characteristics of the time series, but the analysis of the time series signal requires a detrending process, which causes pseudo-fluctuation errors to appear.…”