Research

16 pages, 911 KiB

Open AccessArticle

A Novel Autoencoder with Dynamic Feature Enhanced Factor for Fault Diagnosis of Wind Turbine

by Xiaoyin Nie, Shaoguang Liu and Gang Xie

Electronics 2020, 9(4), 600; https://doi.org/10.3390/electronics9040600 - 31 Mar 2020

Cited by 12 | Viewed by 2634

Due to the complicated operating environment and variable operating conditions, wind turbines (WTs) are extremely prone to failure and the frequency of fault increases year by year. Therefore, the solutions of effective condition monitoring and fault diagnosis are urgently demanded. Since the vibration [...] Read more.

Due to the complicated operating environment and variable operating conditions, wind turbines (WTs) are extremely prone to failure and the frequency of fault increases year by year. Therefore, the solutions of effective condition monitoring and fault diagnosis are urgently demanded. Since the vibration signals contain a lot of health condition information, the fault diagnosis based on vibration signals has received extensive attention and achieved impressive progress. However, in practice, the collected health condition signals are very similar and contain a lot of noise, which makes the fault diagnosis of WTs more challenging. In order to handle this problem, this paper proposes a model called denoising stacked feature enhanced autoencoder with dynamic feature enhanced factor (DSFEAE-DF). Firstly, a feature enhanced autoencoder (FEAE) is constructed through feature enhancement so that the discriminative features can be extracted. Secondly, a feature enhanced factor which is independent of manual judgments is proposed and embedded into the training process. Finally, the DSFEAE-DF, combining one noise adding scheme, stacked FEAEs and dynamic feature enhanced factor, is established. Through experimental comparisons, the superiorities of the proposed DSFEAE-DF are verified. Full article

(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)

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16 pages, 4492 KiB

Open AccessArticle

A Fault Diagnosis Strategy Based on Multilevel Classification for a Cascaded Photovoltaic Grid-Connected Inverter

by Wenyi Yuan, Tianzhen Wang, Demba Diallo and Claude Delpha

Electronics 2020, 9(3), 429; https://doi.org/10.3390/electronics9030429 - 04 Mar 2020

Cited by 15 | Viewed by 3487

Abstract

In this paper, an effective strategy is presented to realize IGBT open-circuit fault diagnosis for closed-loop cascaded photovoltaic (PV) grid-connected inverters. The approach is based on the analysis of the inverter output voltage time waveforms in healthy and faulty conditions. It is mainly [...] Read more.

In this paper, an effective strategy is presented to realize IGBT open-circuit fault diagnosis for closed-loop cascaded photovoltaic (PV) grid-connected inverters. The approach is based on the analysis of the inverter output voltage time waveforms in healthy and faulty conditions. It is mainly composed of two parts. The first part is to select the similar faults based on Euclidean distance and set the specific labels. The second part is the classification based on Principal Component Analysis and Support Vector Machine. The classification is done in two steps. In the first, similar faults are grouped to do the preliminary diagnosis of all fault types. In the second step the similar faults are discriminated. Compared with existing fault diagnosis strategies for several fundamental periods and under different external environments, the proposed strategy has better robustness and higher fault diagnosis accuracy. The effectiveness of the proposed fault diagnosis strategy is assessed through simulation results. Full article

(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)

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22 pages, 6718 KiB

Open AccessArticle

Power Quality Assessment of Grid-Connected PV System in Compliance with the Recent Integration Requirements

by Ali Q. Al-Shetwi, M A Hannan, Ker Pin Jern, Ammar A. Alkahtani and A. E. PG Abas

Electronics 2020, 9(2), 366; https://doi.org/10.3390/electronics9020366 - 21 Feb 2020

Cited by 80 | Viewed by 7564

Abstract

The generation and integration of photovoltaic power plants (PVPPs) into the utility grid have increased dramatically over the past two decades. In this sense, and to ensure a high quality of the PVPPs generated power as well as a contribution on the power [...] Read more.

The generation and integration of photovoltaic power plants (PVPPs) into the utility grid have increased dramatically over the past two decades. In this sense, and to ensure a high quality of the PVPPs generated power as well as a contribution on the power system security and stability, some of the new power quality requirements imposed by different grid codes and standards in order to regulate the installation of PVPPs and ensure the grid stability. This study aims to investigate the recent integration requirements including voltage sag, voltage flicker, harmonics, voltage unbalance, and frequency variation. Additionally, compliance controls and methods to fulfill these requirements are developed. In line with this, a large-scale three-phase grid-connected PVPP is designed. A modified inverter controller without the use of any extra device is designed to mitigate the sage incidence and achieve the low-voltage ride-through requirement. It can efficiently operate at normal conditions and once sag or faults are detected, it can change the mode of operation and inject a reactive current based on the sag depth. A dynamic voltage regulator and its controller are also designed to control the voltage flicker, fluctuation, and unbalance at the point of common coupling between the PVPP and the grid. The voltage and current harmonics are reduced below the specified limits using proper design and a RLC filter. The obtained results show that the proposed controller fulfilled the recent standard requirements in mitigating power quality (PQ) events. Thus, this study can increase the effort towards the development of smooth PVPP integration by optimizing the design, operation and control strategies towards high PQ and green electricity. Full article

(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)

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27 pages, 6442 KiB

Open AccessArticle

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

by Wentao Mao, Di Zhang, Siyu Tian and Jiamei Tang

Electronics 2020, 9(2), 323; https://doi.org/10.3390/electronics9020323 - 13 Feb 2020

Cited by 21 | Viewed by 4009

Abstract

In recent years, machine learning techniques have been proven to be a promising tool for early fault detection of rolling bearings. In many actual applications, however, bearing whole-life data are not easy to be historically accumulated, while insufficient data may result in training [...] Read more.

In recent years, machine learning techniques have been proven to be a promising tool for early fault detection of rolling bearings. In many actual applications, however, bearing whole-life data are not easy to be historically accumulated, while insufficient data may result in training a detection model that is not good enough. If utilizing the available data under different working conditions to facilitate model training, the data distribution of different bearings are usually quite different, which does not meet the precondition of

i n d e p e n d e n t

a n d

i d e n t i c a l

d i s t r i b u t i o n

(

i . i . d

) and tends to cause performance reduction. In addition, disturbed by the unstable noise under complex conditions, most of the current detection methods are inclined to raise false alarms, so that the reliability of detection results needs to be improved. To solve these problems, a robust detection method for bearings early fault is proposed based on deep transfer learning. The method includes offline stage and online stage. In the offline stage, by introducing a deep auto-encoder network with domain adaptation, the distribution inconsistency of normal state data among different bearings can be weakened, then the common feature representation of the normal state is obtained. With the extracted common features, a new state assessment method based on the robust deep auto-encoder network is proposed to evaluate the boundary between normal state and early fault state in the low-rank feature space. By training a support vector machine classifier, the detection model is established. In the online stage, along with the data batch arriving sequentially, the features of target bearing are extracted using the common representation learnt in the offline stage, and online detection is conducted by feeding them into the SVM model. Experimental results on IEEE PHM Challenge 2012 bearing dataset and XJTU-SY dataset show that the proposed approach outperforms several state-of-the-art detection methods in terms of detection accuracy and false alarm rate. Full article

(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)

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26 pages, 2358 KiB

Open AccessArticle

A New Fault Diagnosis Method of Bearings Based on Structural Feature Selection

by Wentao Mao, Liyun Wang and Naiqin Feng

Electronics 2019, 8(12), 1406; https://doi.org/10.3390/electronics8121406 - 25 Nov 2019

Cited by 17 | Viewed by 2483

Abstract

By using signal processing and statistical analysis methods simultaneously, many heterogeneous features can be produced to describe the bearings fault with more comprehensive and discriminant information. At same time, there may exist redundant or irrelevant information which will instead reduce the diagnosis performance. [...] Read more.

By using signal processing and statistical analysis methods simultaneously, many heterogeneous features can be produced to describe the bearings fault with more comprehensive and discriminant information. At same time, there may exist redundant or irrelevant information which will instead reduce the diagnosis performance. To solve this problem, it is necessary to conduct feature selection which tries to choose the most typical and discriminant features by evaluating their effect on fault status. However, if the structural relationship between features has not been considered well, some similar or redundant features are still probably chosen, which would introduce bias into the final diagnosis model. In this paper, a new fault diagnosis method of bearings based on structural feature selection is proposed to solve the aforementioned problem. Obeying the hypothesis that the features with strong relatedness have close coefficient distance, the proposed method aims to improve diagnosis performance via determining group structure in fault features. First, a new feature selection strategy is proposed by introducing a group identification matrix. Using this matrix, two evaluation criteria about intra-group feature correlation and inter-group feature difference are constructed by means of coefficient’s distance. Consequently, we get a multi-objective 0–1 integer programming problem by minimizing intra-group distance and maximizing inter-group distance simultaneously. Second, we use the multi-objective particle swarm optimization algorithm to solve this problem, and then determine the optimal group structure of features adaptively. Finally, a diagnosis model can be trained by support vector machine on the typical features extracted from these groups. Experimental results on four UCI datasets show the effectiveness of the proposed group feature selection strategy. Moreover, the experimental results on two bearing datasets (i.e., CWRU and IMS datasets) demonstrate that the proposed method can identify the inherent group structure in fault features, and then has better diagnosis performance compared with several state-of-the-art methods. Full article

(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)

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19 pages, 6446 KiB

Open AccessArticle

A Life Prediction Model of Flywheel Systems Using Stochastic Hybrid Automaton

by Yuehua Cheng, Bin Jiang, Xiaodong Han and Zhijun Wang

Electronics 2019, 8(11), 1236; https://doi.org/10.3390/electronics8111236 - 29 Oct 2019

Cited by 1 | Viewed by 2138

Abstract

This paper proposes a practical life prediction model for Flywheel Systems (FSs) using the Stochastic Hybrid Automaton (SHA) method. The reliability of motors and the performance degradation of bearings are considered key causes of the failure of FSs. The unit flywheel SHA model [...] Read more.

This paper proposes a practical life prediction model for Flywheel Systems (FSs) using the Stochastic Hybrid Automaton (SHA) method. The reliability of motors and the performance degradation of bearings are considered key causes of the failure of FSs. The unit flywheel SHA model is established for the failure mechanism, considering burst failure of motors and the accumulated performance degradation of bearings. This prediction model also describes the dynamic relation of lifetime with the configurations of FSs, work modes, and running environments. Monte Carlo simulation results demonstrate that the life distributions of FSs are quite different if the spacecrafts run in various orbits or with different configurations, or under changed work modes. The proposed method provides an engineering reference and guidance for the scheme design and in-orbit mission planning of FSs. Full article

(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)

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14 pages, 437 KiB

Open AccessArticle

An Effective Multiclass Twin Hypersphere Support Vector Machine and Its Practical Engineering Applications

by Qing Ai, Anna Wang, Aihua Zhang, Wenhui Wang and Yang Wang

Electronics 2019, 8(10), 1195; https://doi.org/10.3390/electronics8101195 - 20 Oct 2019

Cited by 1 | Viewed by 2223

Abstract

Twin-KSVC (Twin Support Vector Classification for K class) is a novel and efficient multiclass twin support vector machine. However, Twin-KSVC has the following disadvantages. (1) Each pair of binary sub-classifiers has to calculate inverse matrices. (2) For nonlinear problems, a pair of additional [...] Read more.

Twin-KSVC (Twin Support Vector Classification for K class) is a novel and efficient multiclass twin support vector machine. However, Twin-KSVC has the following disadvantages. (1) Each pair of binary sub-classifiers has to calculate inverse matrices. (2) For nonlinear problems, a pair of additional primal problems needs to be constructed in each pair of binary sub-classifiers. For these disadvantages, a new multi-class twin hypersphere support vector machine, named Twin Hypersphere-KSVC, is proposed in this paper. Twin Hypersphere-KSVC also evaluates each sample into 1-vs-1-vs-rest structure, as in Twin-KSVC. However, our Twin Hypersphere-KSVC does not seek two nonparallel hyperplanes in each pair of binary sub-classifiers as in Twin-KSVC, but a pair of hyperspheres. Compared with Twin-KSVC, Twin Hypersphere-KSVC avoids computing inverse matrices, and for nonlinear problems, can apply the kernel trick to linear case directly. A large number of comparisons of Twin Hypersphere-KSVC with Twin-KSVC on a set of benchmark datasets from the UCI repository and several real engineering applications, show that the proposed algorithm has higher training speed and better generalization performance. Full article

(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)

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12 pages, 1356 KiB

Open AccessArticle

Fault Tolerant Control of Electronic Throttles with Friction Changes

by Haiying Qi, Clifford Mayhew, Yujia Zhai, Shuangxin Wang, Dingli Yu, J. Barry Gomm and Qian Zhang

Electronics 2019, 8(9), 918; https://doi.org/10.3390/electronics8090918 - 22 Aug 2019

Viewed by 2453

Abstract

To enhance the reliability of the electronic throttle and consequently the vehicles driven by the internal combustion engines, a fault tolerant control strategy is developed in this paper. The proposed method employs a full-order terminal sliding mode control in conjunction with an adaptive [...] Read more.

To enhance the reliability of the electronic throttle and consequently the vehicles driven by the internal combustion engines, a fault tolerant control strategy is developed in this paper. The proposed method employs a full-order terminal sliding mode control in conjunction with an adaptive radial basis function network to estimate change rate of the fault. Fault tolerant control to abrupt and incipient changes in the throttle viscous friction torque coefficient and the throttle coulomb friction torque coefficient is achieved. Whilst the throttle position is driven to track the reference signal, the post-fault dynamics are guaranteed to converge to the equilibrium point in finite time, and the control is smooth without chattering. A nonlinear Simulink model of an electronic throttle is developed with real physical parameters and is used for evaluation of the developed method. A significant change of the throttle friction torque is simulated, and the fault tolerant control system keeps system stability and tracking the reference signal in the presence of the fault. Full article

(This article belongs to the Special Issue Fault Detection and Diagnosis of Intelligent Mechatronic Systems)

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