Fault diagnosis of planetary gearbox using multi-criteria feature selection and heterogeneous ensemble learning classification

Wang, Zi-Rui; Huang, Haian; Wang, Youren

doi:10.1016/j.measurement.2020.108654

Cited by 42 publications

(18 citation statements)

References 17 publications

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“…The results indicated that the proposed ensemble RBF-SVM outperforms single-method approaches in terms of effectiveness, with a score of 98.46 percent. There are some other studies that includes ensemble learning method for feature selection [261], [262], [263], [264], [265].…”

Section: Ensemble Learning Methodsmentioning

confidence: 99%

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

et al. 2022

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Feature selection is employed to reduce feature dimensions and computational complexity by eliminating irrelevant and redundant features. A vast amount of increasing data and its processing generate many feature sets, that are reduced by the feature selection process to improve the performance in all sorts of classification, regression, clustering models. This research performs a detailed analysis of motivation and concentrates on the fundamental architecture of feature selection. The study aims to establish a structured formation related to popular methods such as filter, wrapper, embedded into search strategies, evaluation criteria, and learning methods. Different methods organize a comparison of benefits and drawbacks followed by multiple classification algorithms and standard validation measures. The diversity of applications in multiple domains such as data retrieval, prediction analysis, and medical, intrusion, and industrial applications are efficiently highlighted. The study focused on some additional feature selection methods for handling big data. Nonetheless, new challenges have surfaced in the analysis of such data, which are also addressed in this study. Reflecting on commonly encountered challenges and clarifying how to obtain the absolute feature selection method are the significant components of this study.

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Section: Ensemble Learning Methodsmentioning

confidence: 99%

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

et al. 2022

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“…The upper value should be set up higher, because of the non-linear nature of damping in bearing-rotor system as explained in [14]- [21] and [11]. For instance, having properly aligned and balanced rotors on the same machine, different state of initial conditions (such as rotor and/or steam temperature, time of stand-still, etc.)…”

Section: Upper and Lower Values For Open Definitionmentioning

confidence: 99%

Fault detection method based on an automated operating envelope during transient states for the large turbomachinery

Zabaryłło¹,

Barszcz²

2021

J. vibroeng.

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In the energy generation business steam powered turbo-generators still play an important role in electrical power generation all over the world. Every facility using steam turbines considers them as the critical machinery. Such machines should be well-maintained, properly handled, and precisely diagnosed in order to achieve the best performance and safety. The most valued data about the technical health are collected during machine's shut-downs and run-ups. These data are more than seldom and hard to assess without expert's knowledge with strong theoretical background and experience. Main novelty of the paper is the automated method for novelty detection of machine's vibration. Most proposed methods apply to smaller machines with rolling bearings, whereas we propose the method for large machines with sliding bearings, which have much different behavior. The application of the method is support of the plant maintenance staff to evaluate deviations of turbo-sets from a healthy state based on the concept which we called the Operating Envelope. The envelope is created based on the data from a vibration sensor during the transient state. In this paper we consider a single vibration sensor and only the first harmonic amplitude of this signal. To set the acceptance limits within which turbo-set's dynamic response will be considered as acceptable, we used the cubic spline interpolation coupled with expert judgement. Beyond these limits the state of the turbo-set is considered as unhealthy, so it is an automated fault detection method. In such a case a machine should be a subject to further and deeper diagnostic analysis. The method was validated on the data from the 13K242 type (a 200 MW class turbine) steam turbine. We also proposed a set of parameters to evaluate the severity of malfunction.

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“…Zhang et al proposed a fault diagnosis method based on time-frequency characteristics and PSO-SVM, and verified that the method can quickly and accurately identify the fault type of planetary gears from nonstationary signals [6]. Wang et al proposed a gear fault diagnosis method based on multicriteria fault feature selection and heterogeneous integrated learning classification, which improved the accuracy and robustness of diagnosis [7]. Aiming at a kind of multimode process with hidden degenerate faults, a fault prediction algorithm based on the combination of multi-PCA model and fault reconstruction technology is proposed, which can well solve the fault prediction problem of multimode process data [8].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Planetary Gear Based on FRWT and 2D-CNN

Jiao

2022

Mathematical Problems in Engineering

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The fault signals of planetary gears are nonstationary and nonlinear signals. It is difficult to extract weak fault features under strong background noise. This paper adopts a new filtering method, fractional Wavelet transform (FRWT). Compared with the traditional fractional Fourier transform (FRFT), it can improve the effect of noise reduction. This paper adopts a planetary gear fault diagnosis method combining fractional wavelet transform (FRWT) and two-dimensional convolutional neural network (2D-CNN). Firstly, several intrinsic mode component functions (IMFs) are obtained from the original vibration signal by AFSA-VMD decomposition, and the two components with the largest correlation coefficient are selected for signal reconstruction. Then, the reconstructed signal is filtered in fractional wavelet domain. By analyzing the wavelet energy entropy of the filtered signal, a two-dimensional normalized energy characteristic matrix is constructed and the two-dimensional features are input into the two-dimensional convolution neural network model for training. The simulation results show that the training effect of this method is better than that of FRFT-2D-CNN. Through the verification of the test set, we can know that the fault diagnosis of planetary gears can be realized accurately based on FRWT and 2D-CNN.

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Fault diagnosis of planetary gearbox using multi-criteria feature selection and heterogeneous ensemble learning classification

Cited by 42 publications

References 17 publications

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

Fault detection method based on an automated operating envelope during transient states for the large turbomachinery

Fault Diagnosis of Planetary Gear Based on FRWT and 2D-CNN

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