Fault feature extraction based on combination of envelope order tracking and cICA for rolling element bearings

Yang, Tangfeng; Guo, Yu; Na, Jing; Fung, Rong‐Fong

doi:10.1016/j.ymssp.2017.03.050

Cited by 37 publications

(16 citation statements)

References 17 publications

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“…So, a feature evaluation method based on information granulation and neighboring clustering is put forward for tree heuristic feature selection (THFS). In this feature evaluation method, information granulation and neighboring clustering were used to delete the features that were obviously ineffective in fault distinguishing at first; then, the remaining features were evaluated through Equations (8)- (11). The specific steps of this feature evaluation method are introduced below.…”

Section: The Basic Concept Of Dfvmentioning

confidence: 99%

“…Feature extraction [8][9][10] and fault recognition are two determinant steps which affected the accuracy of fault diagnosis. Therefore, the related researches mainly focused on feature extraction method [11,12] and fault recognition method. The second kind of methods [13] did not need advance feature extraction and could learn complex fault parameters from the original fault data and realize fault identification autonomously.…”

Section: Introductionmentioning

confidence: 99%

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Rolling Bearings Fault Diagnosis Based on Tree Heuristic Feature Selection and the Dependent Feature Vector Combined with Rough Sets

et al. 2019

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Rolling element bearings (REB) are widely used in all walks of life, and they play an important role in the health operation of all kinds of rotating machinery. Therefore, the fault diagnosis of REB has attracted substantial attention. Fault diagnosis methods based on time-frequency signal analysis and intelligent classification are widely used for REB because of their effectiveness. However, there still exist two shortcomings in these fault diagnosis methods: (1) A large amount of redundant information is difficult to identify and delete. (2) Aliasing patterns decrease the methods’ classification accuracy. To overcome these problems, this paper puts forward an improved fault diagnosis method based on tree heuristic feature selection (THFS) and the dependent feature vector combined with rough sets (RS-DFV). In the RS-DFV method, the feature set was optimized through the dependent feature vector (DFV). Furthermore, the DFV revealed the essential difference among different REB faults and improved the accuracy of fault description. Moreover, the rough set was utilized to reasonably describe the aliasing patterns and overcome the problem of abnormal termination in DFV extraction. In addition, a tree heuristic feature selection method (THFS) was devised to delete the redundant information and construct the structure of RS-DFV. Finally, a simulation, four other feature vectors, three other feature selection methods and four other fault diagnosis methods were utilized for the REB fault diagnosis to demonstrate the effectiveness of the RS-DFV method. RS-DFV obtained an effective subset of five features from 100 features, and acquired a very good diagnostic accuracy (100%, 100%, 99.51%, 100%, 99.47%, 100%), which is much higher than all comparative tests. The results indicate that the RS-DFV method could select an appropriate feature set, deeply dig the effectiveness of the features and more exactly describe the aliasing patterns. Consequently, this method performs better in REB fault diagnosis than the original intelligent methods.

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Section: The Basic Concept Of Dfvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rolling Bearings Fault Diagnosis Based on Tree Heuristic Feature Selection and the Dependent Feature Vector Combined with Rough Sets

et al. 2019

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“…Wang et al [24] proposed a hybrid method based on COT and VMD-based time frequency distribution (TFD) to diagnose rolling bearing fault under variable speed conditions. Aiming at weak feature extraction for rolling bearing diagnosis, Yang et al [25] developed a method which is a combination of the COT and the constrained independent component analysis (cICA). Incipient faults can be detected through the robust method at various shaft speed conditions.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Method Using Spline-Kernelled Chirplet Transform and Angle Synchronous Averaging to Detect Faults at Variable Speed

Wang

Xiang

2019

IEEE Access

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Conventional order tracking, which relies on a reference signal, is a common tool for rotary machinery fault diagnosis under speed fluctuation working conditions. However, it is inconvenient to install a speed sensor under certain circumstances. In this paper, we present a two-stage method to detect variable speed rolling bearing faults without a tachometer. In the first stage, the spline-kernelled chirplet transform (SCT) is applied to calculate the time-frequency distribution and extract the instantaneous rotation frequency. In the second stage, angle synchronous averaging is employed to resample the raw signal and detect fault features. The comparison investigations between SCT and other time-frequency analysis methods are performed by a numerical signal with a nonlinear instantaneous frequency. The experimental studies of variable speed rolling bearings with the outer and inner race faults are further performed. The results demonstrate that the proposed method can well detect rolling bearing faults without a key-phase signal from strong background noise under varying speed conditions. INDEX TERMS General parameterized time-frequency analysis methods, spline-kernelled chirplet transform, angle synchronous averaging, variable speed, bearing fault detection.

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“…A natural idea to the multiple-faults condition is to collect vibration signals by some sensors installed somewhere in the machinery, and then separate out every fault signal from collected signals by blind source separation (BSS) methods [6]. In the past two decades, some researchers have been studying bearing or (and) gear fault detection using some BSS type methods, such as blind deconvolution method, blind equalization method, fast independent component analysis (fastICA) method, constrained independent component analysis (cICA) method [7]- [11]. These BSS methods all utilized collected multiple-channels sensor signal to separate out various fault signals and then analyze their frequency characteristics.…”

Section: Introductionmentioning

confidence: 99%

Fault Separation and Detection for Compound Bearing-Gear fault Condition Based on Decomposition of Marginal Hilbert Spectrum

et al. 2019

IEEE Access

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For the rotating machinery system, it is a challenge to explore fault detection and diagnosis for multiple-faults condition, which simultaneously contains faulty bearing components and faulty gear components. In the study, a fault feature separation and extraction approach is proposed for the bearing-gear fault condition through combining empirical mode decomposition (EMD), Hilbert transform (HT), principal component analysis (PCA), independent component analysis (ICA) techniques. Firstly, EMD is implemented to decompose the single sensor signal to obtain multiple sub-band signals termed as the intrinsic mode functions (IMFs). Secondly, the most relevant IMFs to bearing and gear fault features are selected to construct multiple-channels model with the help of the simulated bearing and gear fault signals. Thirdly, HT is utilized to compute marginal Hilbert spectrum (MHS) for each IMF in multiple-channels model, to construct an MHS matrix. Finally, some statistically independent components are obtained by decomposing the MHS matrix with PCA and ICA, and multiple fault features are identified from these components. The experimental application of the proposed method is put into a bearing-belt-gearbox union machinery system to evaluate its validity. The experimental analysis results indicate that the proposed method is effective to separate and extract a bearing fault and a gear fault for two types of compound bearing-gear fault conditions. INDEX TERMS Empirical mode decomposition (EMD), Hilbert transform (HT), independent component analysis (ICA), marginal Hilbert spectrum (MHS), multiple faults detection. XUELI ZHU received the B.Sc. degree in construction industry enterprise automation from Construction Industry Enterprise Automation, in 1982, and the Ph.D. degree in heating, gasing, ventilation, and air conditioning engineering from the

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Fault feature extraction based on combination of envelope order tracking and cICA for rolling element bearings

Cited by 37 publications

References 17 publications

Rolling Bearings Fault Diagnosis Based on Tree Heuristic Feature Selection and the Dependent Feature Vector Combined with Rough Sets

Rolling Bearings Fault Diagnosis Based on Tree Heuristic Feature Selection and the Dependent Feature Vector Combined with Rough Sets

A Two-Stage Method Using Spline-Kernelled Chirplet Transform and Angle Synchronous Averaging to Detect Faults at Variable Speed

Fault Separation and Detection for Compound Bearing-Gear fault Condition Based on Decomposition of Marginal Hilbert Spectrum

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