Intelligent Fault Diagnosis of Diesel Engines via Extreme Gradient Boosting and High-Accuracy Time–Frequency Information of Vibration Signals

Accurate and timely misfire fault diagnosis is of vital significance for diesel engines. However, existing algorithms are prone to fall into model over-fitting and adopt low energy-concentrated features. This paper presents a novel extreme gradient boosting-based misfire fault diagnosis approach utilizing the high-accuracy time–frequency information of vibration signals. First, diesel engine misfire tests were conducted under different spindle speeds, and the corresponding vibration signals were acquired via a triaxial accelerometer. The time-domain features of signals were extracted by using a time-domain statistics method, while the high-accuracy time–frequency domain features were obtained via the high-resolution multisynchrosqueezing transform. Thereafter, considering the nonlinearity and high dimensionality of the original characteristic data sets, the locally linear embedding method was employed for feature dimensionality reduction. Eventually, to avoid model overfitting, the extreme gradient boosting algorithm was utilized for diesel engine misfire fault diagnosis. Experiments under different spindle speeds and comprehensive comparisons with other evaluation methods were conducted to demonstrate the effectiveness of the proposed extreme gradient boosting-based misfire diagnosis method. The results verify that the highest classification accuracy of the proposed extreme gradient boosting-based algorithm is up to 99.93%. Simultaneously, the classification accuracy of the presented approach is approximately 24.63% higher on average than those of algorithms that use wavelet packet-based features. Moreover, it is shown that it obtains the minimum root mean squared error and can effectively prevent the model from falling into overfitting.

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Fault diagnosis of internal combustion engine gearbox using vibration signals based on signal processing techniques

Journal of Quality in Maintenance Engineering ◽

10.1108/jqme-11-2019-0109 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Ravikumar KN ◽

Hemantha Kumar ◽

Kumar GN ◽

Gangadharan KV

Keyword(s):

Signal Processing ◽

Fault Diagnosis ◽

Classification Accuracy ◽

Internal Combustion ◽

Frequency Characteristics ◽

Ic Engine ◽

Vibration Signals ◽

Content Type ◽

Time Frequency ◽

Gear Fault

PurposeThe purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.Design/methodology/approachVibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.FindingsThe obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.Practical implicationsThe proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.Originality/valueIn this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

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Diesel Engine Valve Clearance Fault Diagnosis Based on Improved Variational Mode Decomposition and Bispectrum

Energies ◽

10.3390/en12040661 ◽

2019 ◽

Vol 12 (4) ◽

pp. 661 ◽

Cited By ~ 6

Author(s):

Xiaoyang Bi ◽

Shuqian Cao ◽

Daming Zhang

Keyword(s):

Fault Diagnosis ◽

Diesel Engine ◽

Frequency Domain ◽

Time Domain ◽

Frequency Domain Analysis ◽

Variational Mode Decomposition ◽

Vibration Signals ◽

Engine Valve ◽

Mode Decomposition ◽

Reconstructed Signal

The evaluation and fault diagnosis of a diesel engine’s health conditions without disassembly are very important for diesel engine safe operation. Currently, the research on fault diagnosis has focused on the time domain or frequency domain processing of vibration signals. However, early fault signals are mostly weak energy signals, and the fault information cannot be completely extracted by time domain and frequency domain analysis. Thus, in this article, a novel fault diagnosis method of diesel engine valve clearance using the improved variational mode decomposition (VMD) and bispectrum algorithm is proposed. First, the experimental study was designed to obtain fault vibration signals. The improved VMD method by choosing the optimal decomposition layers is applied to denoise vibration signals. Then the bispectrum analysis of the reconstructed signal after VMD decomposition is carried out. The results show that bispectrum image under different working conditions exhibits obviously different characteristics respectively. At last, the diagonal projection method proposed in this paper was used to process the bispectrum image, and the fourth order cumulant is calculated. The calculation results show that three states of the valve clearance are successfully distinguished.

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A Novel Improved Local Binary Pattern and Its Application to the Fault Diagnosis of Diesel Engine

Shock and Vibration ◽

10.1155/2020/9830162 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Yanping Cai ◽

Guanghua Xu ◽

Aihua Li ◽

Xu Wang

Keyword(s):

Fault Diagnosis ◽

Diesel Engine ◽

Local Binary Pattern ◽

Nearest Neighbor ◽

Spatial Information ◽

Wavelet Packet ◽

Texture Feature ◽

Support Vector ◽

Vibration Signals ◽

Time Frequency

Aiming at the feature extraction difficulty of vibration signals, an improved local binary pattern- (ILBP-) based diesel engine fault diagnosis approach is proposed. To effectively make use of the component spatial information in time-frequency images, local binary pattern (LBP) algorithm is applied. Also, in view of the problems that traditional LBP coding is easily interfered by singular pixel points and the relative spatial information is not prominent, an improved coding rule of the LBP operator is put forward in this paper. Compared with some typical LBP algorithms, computational complexity of the proposed ILBP algorithm is greatly reduced, and the coding sparsity is greatly improved. The ILBP operator is applied to fault diagnosis of BF4L1011F diesel engine with eight different valve conditions. For comparison, six kinds of time-frequency distribution are used to convert raw vibration signals into time-frequency images, and then circular LBP, rotation-invariant LBP, uniform LBP, and ILBP operator are applied for texture coding. Finally, nearest neighbor classifier (NNC) and support vector machine (SVM) are used for fault identification. The classification results show that the ILBP operator proposed in this paper can better describe the texture feature information in vibration time-frequency images of the diesel engine, and a good diagnostic effect can be achieved by combining wavelet packet (WP) distribution and ILBP.

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Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

Journal of Vibration and Acoustics ◽

10.1115/1.4043508 ◽

2019 ◽

Vol 141 (5) ◽

Author(s):

Wei Xiong ◽

Qingbo He ◽

Zhike Peng

Keyword(s):

Fault Diagnosis ◽

Time Domain ◽

Feature Matching ◽

Matching Pursuit ◽

Microphone Array ◽

Time Frequency ◽

Moving Sources ◽

The Time Domain ◽

Envelope Spectrum ◽

Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

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Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

Shock and Vibration ◽

10.1155/2018/1063645 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Fan Jiang ◽

Zhencai Zhu ◽

Wei Li ◽

Bo Wu ◽

Zhe Tong ◽

...

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Optimal Number ◽

Permutation Entropy ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

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Multiresolution analysis of vibration signals acquired from locomotive Diesel engine for classification of engine states basing on signal statistical parameters

Combustion Engines ◽

10.19206/ce2017-111 ◽

2017 ◽

Vol 168 (1) ◽

pp. 68-72

Author(s):

Piotr BOGUŚ ◽

Mateusz CIESZYŃSKI ◽

Jerzy MERKISZ

Keyword(s):

Diesel Engine ◽

Complex Analysis ◽

Statistical Parameters ◽

Vibration Signals ◽

Time Frequency ◽

Significant Difference ◽

Before And After ◽

On Line ◽

Locomotive Diesel

The paper presents a method of classification of locomotive Diesel engine states basing on vibration signals taken from an engine body and using chosen statistical parameters calculated for the original signal and it wavelet multiresolution components. The researches presented in the paper concern estimation of an engine states before and after a general repair. The target application of the presented researches is an on-line diagnostic system which can complement standard OBD systems. To this purpose the applied methods should not base on complex analysis of some spectral, time-frequency or scalogram plots but rather on choosing single diagnostic parameters which are suitable for the fast on-line diagnostic. The results have showed the significant difference in distinguishing of engine work before and after a general repair using some chosen statistical parameters applied to vibration signals.

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Identification of Fault Signals in Rotating Machinery Using Higher Order Time Frequency Analysis

Volume 1D: 16th Biennial Conference on Mechanical Vibration and Noise ◽

10.1115/detc97/vib-4137 ◽

1997 ◽

Author(s):

Sang-Kwon Lee ◽

Paul R. White

Keyword(s):

Fault Diagnosis ◽

Wigner Distribution ◽

Higher Order ◽

Rotating Machinery ◽

Data Sets ◽

Higher Order Statistics ◽

Vibration Signals ◽

Time Frequency ◽

Increasing Trend ◽

Monitoring Application

Abstract Impulsive acoustic and vibration signals within rotating machinery are often induced by irregular impacting. Thus the detection of these impulses can be useful for fault diagnosis. Recently there is an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals tend to increase the kurtosis values. We show that the fourth order Wigner Moment Spectrum, called the Wigner Trispectrum, has superior detection performance to second order Wigner distribution for typical impulsive signals found in a condition monitoring application. These methods are also applied to data sets measured within a car engine and industrial gearbox.

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Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Applied Sciences ◽

10.3390/app10207068 ◽

2020 ◽

Vol 10 (20) ◽

pp. 7068

Author(s):

Minh Tuan Pham ◽

Jong-Myon Kim ◽

Cheol Hong Kim

Keyword(s):

Acoustic Emission ◽

Feature Extraction ◽

Deep Learning ◽

Fault Diagnosis ◽

Extraction Methods ◽

High Accuracy ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Ae Signals

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

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Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD

Applied Sciences ◽

10.3390/app10186376 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6376 ◽

Cited By ~ 1

Author(s):

Yihan Wang ◽

Zhonghui Fan ◽

Hongmei Liu ◽

Xin Gao

Keyword(s):

Fault Diagnosis ◽

Information Entropy ◽

Entropy Method ◽

Characteristic Matrix ◽

Intrinsic Mode Functions ◽

Noise Component ◽

Time Frequency ◽

Frequency Information ◽

Operation Conditions ◽

Mode Decomposition

Planetary gearboxes are more and more widely used in large and complex construction machinery such as those used in aviation, aerospace fields, and so on. However, the movement of the gear is a typical complex motion and is often under variable conditions in real environments, which may make vibration signals of planetary gearboxes nonlinear and nonstationary. It is more difficult and complex to achieve fault diagnosis than to fix the axis gearboxes effectively. A fault diagnosis method for planetary gearboxes based on improved complementary ensemble empirical mode decomposition (ICEEMD)-time-frequency information entropy and variable predictive model-based class discriminate (VPMCD) is proposed in this paper. First, the vibration signal of planetary gearboxes is decomposed into several intrinsic mode functions (IMFs) by using the ICEEMD algorithm, which is used to determine the noise component by using the magnitude of the entropy and to remove the noise components. Then, the time-frequency information entropy of intrinsic modal function under the new decomposition is calculated and regarded as the characteristic matrix. Finally, the fault mode is classified by the VPMCD method. The experimental results demonstrate that the method proposed in this paper can not only solve the fault diagnosis of planetary gearboxes under different operation conditions, but can also be used for fault diagnosis under variable operation conditions. Simultaneously, the proposed method is superior to the wavelet entropy method and variational mode decomposition (VMD)-time-frequency information entropy.

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