Quantitative Diagnosis of Rotor Vibration Fault Using Process Power Spectrum Entropy and Support Vector Machine Method

Abstract

To improve the diagnosis capacity of rotor vibration fault in stochastic process, an effective fault diagnosis method (named Process Power Spectrum Entropy (PPSE) and Support Vector Machine (SVM) (PPSE-SVM, for short) method) was proposed. The fault diagnosis model of PPSE-SVM was established by fusing PPSE method and SVM theory. Based on the simulation experiment of rotor vibration fault, process data for four typical vibration faults (rotor imbalance, shaft misalignment, rotor-stator rubbing, and pedestal looseness) were collected under multipoint (multiple channels) and multispeed. By using PPSE method, the PPSE values of these data were extracted as fault feature vectors to establish the SVM model of rotor vibration fault diagnosis. From rotor vibration fault diagnosis, the results demonstrate that the proposed method possesses high precision, good learning ability, good generalization ability, and strong fault-tolerant ability (robustness) in four aspects of distinguishing fault types, fault severity, fault location, and noise immunity of rotor stochastic vibration. This paper presents a novel method (PPSE-SVM) for rotor vibration fault diagnosis and real-time vibration monitoring. The presented effort is promising to improve the fault diagnosis precision of rotating machinery like gas turbine.

Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals

“…To verify the effectiveness of the developed MDERF model in inter-shaft bearing fault diagnosis, five fault diagnosis algorithms, i.e., support vector machine (SVM) [8,27], k-nearest neighbor (KNN) [28], classification and regression tree (CART) [29], and gradient boosting decision-tree (GBDT) [30], MDERF, are validated by learning and testing with the same test samples. The performance comparison results of five fault diagnosis algorithms based on test set data are shown in Table 4.…”

“…Conversely, the more confused it is, the higher the information entropy is. Therefore, information entropy can also be said to be a measure of the systematic ordering degree [8]. The information entropy of the normal bearing is lower than the fault bearing, therefore, we can use it to evaluate the bearing working status.…”

“…From the inter-shaft bearing fault simulation experiment, AE signal samples of ball fault, inner race fault, outer race fault, and normal statues are collected. SSE, PSE, WESE, and WSFSE of AE signals of the four faults are structured in terms of Equations (1) and (8). Each of the curves in Figure 6 is the SSE of a fault at multiple rotational speeds.…”

“…As some faults have similar signal characteristics, information entropy can only measure their complexity and perform preliminary noise reduction and classification but cannot accurately classify. Recently, information entropy has been widely applied as the feature extraction method of fault diagnosis [8][9][10][11][12][13][14]. The purpose of fault diagnosis is to predict the discrete values at different fault states of a diagnostic object.…”

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

“…Shannon entropy is used to measure the uniformity of exhaust temperature and vibration data in [144,150]. The fusion of process power spectrum entropy and support vector is proposed to realize four typical faults, including rotor imbalance, shaft misalignment, rotor-stator rubbing, and pedestal looseness [151]. Wavelet correlation feature scale entropy and fuzzy support vector machine are used to analyze the whole-body vibration signal of aero-engine [152].…”