Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features

Abstract

Multisensor information fusion, when applied to fault diagnosis, the time-space scope, and the quantity of information are expanded compared to what could be acquired by a single sensor, so the diagnostic object can be described more comprehensively. This paper presents a methodology of fault diagnosis in rotating machinery using multisensor information fusion that all the features are calculated using vibration data in time domain to constitute fusional vector and the support vector machine (SVM) is used for classification. The effectiveness of the presented methodology is tested by three case studies: diagnostic of faulty gear, rolling bearing, and identification of rotor crack. For each case study, the sensibilities of the features are analyzed. The results indicate that the peak factor is the most sensitive feature in the twelve time-domain features for identifying gear defect, and the mean, amplitude square, root mean square, root amplitude, and standard deviation are all sensitive for identifying gear, rolling bearing, and rotor crack defect comparatively.

Datum unit optimization for robustness of a journal bearing diagnosis system

“…Common fault types in rotating machinery include unbalance, misalignment, rubbing, cracking, and bearing failures. 1,2 Although most of the faults in rotating systems can be identified by diagnostic specialists who visually inspect the spectral analysis of various signals, the need for an automated and reliable diagnosis system is steadily increasing. Automated diagnosis using data-driven techniques can enable detection of anomalies during early stages and can thus contribute to improved safety and increased cost savings.…”

A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management

“…An intelligent pattern classifier algorithm was used for fault detection and decision making. Jiang et al [62] fused time-domain features at the feature level to classify faults of a roller bearing using a Support Vector Machine algorithm.…”

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

“…For example, Ben et al [12] proposed the use of empirical mode decomposition and energy entropy for feature extraction, which was combined with an ANN for multifeature fusion to make bearing fault predictions. Jiang et al [13] used a variety of different time-domain analytical methods for feature extraction combined with SVM for multifeature fusion to achieve fault prediction for rotating machinery. Su et al [14] proposed a new information fusion framework based on convolutional neural networks (CNNs), and residual squeeze networks were used to make fault predictions for high-speed trains.…”