Weak Fault Feature Research Articles

Research on gearbox compound fault diagnosis method and system development based on entire gearbox health maintenance

In general, gearbox is prone to occur compound fault due to its harsh working environment and its fault vibration signal contains multi-components which correspond to each gearbox parts. As the multi-components are often coupled with each other and accompanied by strong noise which brings great difficulties to diagnose fault, however, the existing diagnosis methods are mainly applied on single fault rather than the entire gearbox health maintenance, therefore, this paper presents a gearbox compound fault diagnosis method and develops a diagnosis system which has potential value for gearbox health maintenance. In specific, on account of the morphological difference between multi-components, this paper uses resonance sparse signal decomposition (RSSD) to decompose the fault vibration signal into high and low resonance components respectively for achieving gearbox compound fault separation. Furthermore, as for low resonance component containing rolling bearing fault information, a weak fault feature extraction algorithm based on singular value decomposition (SVD) and cepstrum pre-whitening stochastic resonance is proposed, besides, aiming at the high resonance component containing gear fault information, an early gear fault warning algorithm based on local mean decomposition and two-dimensional approximate entropy of chaotic oscillator is also given. Finally, a gearbox fault diagnosis system, which has the ability such as the gearbox vibration signal acquisition, fault indicator warning, health status evaluation, fault signal storage is developed. Simulation validation and comparison prove the effectiveness of proposed method in this paper.

Noise-boosted weak signal detection in fractional nonlinear systems enhanced by increasing potential-well width and its application to mechanical fault diagnosis

Noise is seen as the annoying something for weak signal detection, but noise is beneficial to enhance weak signals of interest embedded in noise in nonlinear systems. Moreover, the fractional-order derivative can reinforce noise-boosted weak signal detection. However, the role of varying potential-well depth and width individually on noise-boosted weak signal detection in overdamped and underdamped fractional-order nonlinear systems has not been investigated yet. For this purpose, this paper designs a special bistable potential with varying potential-well depth and width individually to study their influences on weak signal detection numerically. Then, based on simulated conclusions a noise-boosted weak fault diagnosis method enhanced by increasing potential-well width is proposed to enhance weak fault characteristics of machinery, where the amplitude amplification factor is seen as an indicator to quantify the performance of weak signal detection. Finally, some simulations and a bearing fault experiment with an outer race defect were performed to demonstrate the proposed method. Simulated conclusions are identical to experimental ones and indicate that increasing potential-well width would improve noise-boosted weak signal detection but increasing potential-well depth would weaken it regardless of underdamped or overdamped fractional-order nonlinear systems. Nonetheless, increasing the potential-well width endlessly would make the amplitude at the outer race fault characteristic frequency unchanged. That is because the signal with the constant energy would not accelerate the particles to jump across the wide potential wells adequately, where it is not an optimal SR. The compared results with a classical denoising method demonstrate the superiority of the proposed method further.

An enhanced cyclostationary method and its application on the incipient fault diagnosis of induction motors

The cyclostationary analysis techniques have been extensively explored for the purpose of fault detection in rotating machinery. However, there are still huge challenges because of both limited detection frequency range and low fault identification accuracy. This paper proposes an improved cyclostationary method to enhance incipient fault features. Firstly, the continuous wavelet transform is used to accurately locate important frequency bands, and the fault modulation mechanism or fast kurtogram can be adopted to design the optimal wavelet transform scale factor. Secondly, the Teager-Kaiser energy operator is improved to be used in the frequency domain for the weak fault feature enhancement. Finally, fault features are presented in the cyclic frequency domain through spectral coherence and enhanced envelope spectrum. The proposed method is verified through both numerical simulation and experiments, including incipient half-broken rotor bar, and rolling bearing outer race faults in induction motors.

Weak Fault Feature Extraction Method of Rolling Bearings Based on MVO-MOMEDA Under Strong Noise Interference

Weak Fault Feature Extraction Method of Rolling Bearings Based on MVO-MOMEDA Under Strong Noise Interference

Resampling Technique-Based Demodulation Analysis for Planet Bearing Cage Fault Diagnosis Under Nonstationary Conditions

Vibration signals of the planetary transmission system usually contain excitation responses from different structures. The fault characteristic of the planet bearing cage is weak, which is often overwhelmed by strong noise and other irrelevant vibration components. Therefore, fault feature extraction of the planet bearing cage is a challenging topic. In this paper, a novel fault feature extraction method is proposed to extract weak fault features of the planet bearing cage. The general parameterized time-frequency transform is employed to accurately extract instantaneous rotational speed information from the planet bearing vibration signal for resampling. It can obtain the accurate rotational speed result even though under power frequency interference conditions. The influence of the deterministic components excited by the gear can be reduced by using the spectral pre-whitening and the interference of noise and random impact can be reduced in the selection of the optimal resonance frequency band by using autogram analysis. The fault characteristic components of the planet bearing cage can be extracted from the square envelop spectrum. The neighborhood power density ratio index is further constructed to distinguish the health bearing state from the cage fault bearing state and evaluate the degree of faults. Experiments designed in a planetary transmission system test rig of an armored vehicle have verified the potential and effectiveness of the proposed fault feature extraction method in exhibiting planet bearing cage fault features.

A Refined Multiscale Symbolic Diverse Entropy Technique for Detecting Weak Faults in Rotating Machinery

In rotating machinery components, it is difficult to extract incipient weak fault features directly from vibration signals. A fault feature extraction method based on refined multiscale symbolic dispersion entropy (RMSDE) is proposed. This method mainly combines symbolic coding and dynamic complexity evaluation to achieve multi-dimensional feature extraction. By constructing multiple sets of simulation signals to evaluate the correlation performance of the proposed method, the superior performance in complexity estimation, anti-noise interference and computational efficiency is mainly demonstrated. When RMSDE is combined with a self-organizing fuzzy logic classifier (SOF), the results show that the proposed RMSDE-SOF method can achieve high-precision fault diagnosis of rotating machinery. The effectiveness and robustness of the proposed method in fault feature extraction and diagnosis are verified by using the rolling bearing dataset and gear dataset, which contain multiple fault types, and the comprehensive performance of the proposed method is better than that of similar feature extraction methods. The final results show that the proposed method is superior to the other methods in rotating machinery fault diagnosis.

Research on SVM-Based Bearing Fault Diagnosis Modeling and Multiple Swarm Genetic Algorithm Parameter Identification Method

The bearing fault diagnosis of petrochemical rotating machinery faces the problems of large data volume, weak fault feature signal strength and susceptibility to noise interference. To solve these problems, current research presents a combined ICEEMDAN-wavelet threshold joint noise reduction, mutual dimensionless metrics and MPGA-SVM approach for rotating machinery bearing fault diagnosis. Firstly, we propose an improved joint noise-reduction method of an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet thresholding. Moreover, the noise-reduced data are processed by mutual dimensionless processing to construct a mutual dimensionless index sensitive to bearing fault features and complete the fault feature extraction of the bearing signals. Furthermore, we design experiments on faulty bearings of multistage centrifugal fans in petrochemical rotating machinery and processed the input data set according to ICEEMDAN-wavelet threshold joint noise reduction and mutual dimensionless indexes for later validation of the model and algorithm. Finally, a support vector machine model used to effectively identify the bearing failures, and a multi-population genetic algorithm, is studied to optimize the relevant parameters of the support vector machine. The powerful global parallel search capability of the multigroup genetic algorithm is used to search for the penalty factor c and kernel parameter r that affect the classification performance of the support vector machine. The global optimal solutions of c and r are found in a short time to construct a multigroup genetic algorithm-support vector machine bearing fault diagnosis and identification model. The proposed model is verified to have 95.3% accuracy for the bearing fault diagnosis, and the training time is 11.1608 s, while the traditional GA-SVM has only 89.875% accuracy and the training time is 17.4612 s. Meanwhile, to exclude the influence of experimental data on the specificity of our method, the experimental validation of the Western Reserve University bearing failure open-source dataset was added, and the results showed that the accuracy could reach 97.1% with a training time of 14.2735 s, thus proving that the method proposed in our paper can achieve good results in practical applications.

Sparse representation learning for fault feature extraction and diagnosis of rotating machinery

Early fault feature extraction and fault diagnosis are of great importance for predictive maintenance of rotating machinery. To accurately extract early fault features from original noisy signals, a novel joint sparse representation learning method is developed in this paper, this method is based on the proposed nonlocal generalized minimax-concave (GMC) penalty and generalized fraction-order total variation (FTV) regularization. The motivation for this research is to leverage the benefits of joint regularizations. The proposed nonlocal GMC penalty regularization tends to preserve weak fault features, promote sparsity and avoid underestimating the amplitude of periodic fault impulses. Simultaneously, the proposed generalized FTV regularization tends to remove fault irrelevant noise and reduce staircase artifacts. Therefore, the proposed model can effectively extract early fault features from original noisy signals. The performance of the proposed model is verified by a series of experiments. In two fault diagnosis tasks, the peak signal-to-noise ratio (PSNR) of the proposed method reaches − 5 dB and − 8 dB, respectively. Compared with state-of-the-art methods, the PSNR has been improved by at least 2 dB, comparison results show that the proposed model has superior performance for early fault feature extraction.

Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage Method

Timely and effective feature extraction is the key for fault diagnosis of rolling element bearing (REB). However, fault feature extraction will become very difficult in the early weak fault stage of REB due to the interference of strong background noise. To solve the above difficulty, a two-stage feature extraction method for early weak fault of REB is proposed, which mainly combines feature mode decomposition (FMD) with a blind deconvolution (BD) method. Firstly, based on the impulsiveness and cyclostationary characteristics of the vibration signal of faulty REB, FMD is used to decompose the complex original vibration signal into several modes containing single component. Subsequently, the sparse index (SI) is calculated for each mode, and the mode containing sensitive fault feature is selected for further analysis. Subsequently, apply the deconvolution method on the selected mode for further enhancing the impulsive characteristic. At last, traditional envelope spectrum (ES) analysis is applied on the filtered signal, and satisfactory fault features are extracted. Effectiveness and advantages of the proposed method are verified through experimental and engineering signals of REBs.

Interpretable sparse learned weights and their entropy based quantification for online machine health monitoring

Incipient fault detection and diagnosis provide a firm grounding for machine health monitoring. Nevertheless, fault signatures at the time of an incipient fault are extremely weak and easily submerged by interference. Currently, signal processing based approaches and machine learning based approaches have been studied for incipient fault detection and diagnosis. However, the former requires relevant technicians with expert knowledge, while the latter needs a substantial volume of labeled samples without model interpretability. In this study, a physics-informed learning framework that integrates weight-based sparse degradation modeling with entropy based indicators is proposed to realize online incipient fault detection and diagnosis. Firstly, based on available normal baseline data and real-time data, a weight-based sparse degradation model is proposed to continually update physics-informed model weights so that weak fault characteristics indicated by learned weights can be considerably enhanced. Meanwhile, this study introduces a family of entropy based indicators for machine health monitoring and their performances are thoroughly investigated based on simulation and experimental studies, which aims to quantify amplified fault characteristics revealed by the continuously updated model weights for online incipient fault detection. Two case studies show that the proposed methodology has better detection ability and sensitivity than classical health indicators for incipient bearing faults. Since the proposed methodology does not demand fault data for model establishment, it is closer to real engineering applications and has more engineering meanings. Moreover, physics-informed model weights can automatically capture informative frequencies for immediate diagnosis and further analysis.

Cyclogram: an effective method for selecting frequency bands for fault diagnosis of rolling element bearings

Frequency band selection for repetitive transient extraction using the kurtogram and its variants plays a vital role in fault diagnosis of rolling element bearings. However, cyclostationarity, one of the most typical symptoms of faulty bearings, is always neglected in these methods, leading to failure of the extraction of the weak fault features. To address this shortcoming, a novel method for selecting frequency bands, called Cyclogram, is here proposed based on kurtosis and cyclostationarity. In the proposed method, a signal is decomposed into several signals in different frequency bands by a wavelet packet transform, and squared envelopes (SEs) are calculated for these decomposed signals. Then, a robust indicator of SEs for evaluating repetitive transients is constructed based on cyclic spectral coherence and kurtosis, which helps to select useful frequency bands. Afterwards, the envelope spectrum of these selected frequency bands are averaged rather than only selecting one frequency band to enhance fault features. Compared with traditional fault-diagnosis methods for rolling element bearings, the proposed method is able to identify faults from signals corrupted seriously with Gaussian and non-Gaussian noise. The effectiveness of Cyclogram is validated based on simulation and three real-world vibration signals from faulty bearings.

Compound fault diagnosis method for rolling bearings based on the multipoint kurtosis spectrum and AO-MOMDEA

In compound rolling bearing faults, different types of fault signals are coupled and cross-affected, which makes it difficult to separate and extract weak fault features, and making it easy to miss diagnoses or misdiagnose. Aiming at solving this problem, a compound fault diagnosis method for rolling bearings based on the multipoint kurtosis spectrum (MKS) and the aquila optimizer multipoint optimal minimum entropy deconvolution adjusted (AO-MOMEDA) method is proposed. Variational modal decomposition is used to reduce the noise of the original vibration signal and improve the recognition of the MKS so as to predict the fault type and preset the search range of the initial period value. A new fault feature strength index with relative amplitude difference is proposed and, combined with the kurtosis of the envelope spectrum, a multi-objective optimization function is constructed. Through the excellent optimization characteristics of the aquila optimizer, the optimal initial period value and filter length are automatically obtained. The optimized MOMEDA method is used for optimal deconvolution of different types of fault signals. Envelope demodulation of the deconvolution signal is carried out to obtain the 1.5-dimensional spectrum of the envelope signal. The types of rolling bearing composite faults are identified according to the fault features in the 1.5-dimensional spectrum. The feasibility of the proposed method is verified through measurements of the signals of rolling bearings with three different types of compound faults. The experimental results show that the proposed method can overcome the influence of strong fault signals and improve the accuracy of compound fault identification. Our results have application value in engineering practice.

A local spectrum enhancement‐based method and its application in incipient fault diagnosis of rotating machinery

AbstractIncipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods. To address this challenge, this paper proposes an effective local spectrum enhancement‐based diagnostic method that can identify weak fault frequencies in the original complicated raw signals. For this purpose, a traversal frequency band segmentation technique is first proposed for dividing the raw signal into a series of subfrequency bands. Then, the proposed synthetic quantitative index is constructed for selecting the most informative local frequency band (ILFB) containing fault features from the divided subfrequency bands. Furthermore, an improved grasshopper optimization algorithm‐based stochastic resonance (SR) system is developed for enhancing weak fault features contained in the selected most ILFB with less computation cost. Finally, the enhanced weak fault frequencies are extracted from the output of the SR system using a common spectrum analysis. Two experiments on a laboratory planetary gearbox and an open bearing data set are used to verify the effectuality of the proposed method. The diagnostic results demonstrate that the proposed method can identify incipient faults of gears and bearings in an effective and accurate manner. Furthermore, the advantages of the proposed method are highlighted by comparison with other methods.

Bearing Weak Fault Feature Extraction Under Time-Varying Speed Conditions Based on Frequency Matching Demodulation Transform

Bearing weak fault feature extraction under time-varying speed conditions is a challenging task. The classic time-frequency analysis (TFA) based ridge detection algorithms cannot work well for detecting time-varying fault characteristic frequency (FCF) harmonics of the bearing due to the limitation of time-frequency resolution and noise interference. As such, a novel frequency matching demodulation transform (FMDT) technique is developed by extending the generalized demodulation transform for bearing weak fault feature extraction and diagnosis under variable speeds. The main novelties of this article are concluded that: the FMDT can first estimate FCF under strong noise conditions by generating a series of demodulation parameters to match FCF with the criterion that the maximal peak can be captured at a specific frequency from the spectra of the demodulated signals, and then calculate rotational frequency (RF) harmonics; and the proposed FMDT-based method does not require the measured bearing RF, and can directly determine bearing fault type using the estimated FCF and RF harmonics. The effectiveness of the FMDT-based bearing fault diagnosis method is verified by both simulated and experimental results. Comparison analysis shows that the proposed method can generate much better results than the classic TFA-based ridge detection algorithms.

Untrained Compound Fault Diagnosis for Planetary Gearbox Based on Adaptive Learning VMD and DSSECNN

Fault coupling and fault override are common phenomena when faults occur in different parts of the planetary gearbox. Labeled compound fault samples are very rare or even unavailable in industrial scenarios. Thus, it is a challenging task to decouple the compound fault and detect the compound fault with unequal severity only utilizing single faults. In this paper, an untrained planetary gearbox compound fault diagnosis method based on Adaptive Learning Variational Mode Decomposition (ALVMD) and Dual Scale Squeeze-and-Excitation Convolutional Neural Network (DSSECNN) is proposed. An ALVMD algorithm is established to enhance weak fault characteristics and enrich the diversity of sample information. The DSSECNN intelligent fault diagnosis model is presented, and the lightweight SENet can enhance the sensitivity of the model to channel features. Moreover, the mapping relationship between the compound fault mode and single faults is revealed through the proposed probability formula, which can reduce the dependence of the training model on compound fault samples. Experiments have been performed on the gearbox fault experiment bench in the laboratory to verify the effectiveness and generalization performance of the method. Besides, wind turbine gearbox test results show that the ALVMD-DSSECNN can achieve the highest average accuracy of 98.83% compared with other related methods in variable working conditions, which has a certain guiding significance for practical engineering.

Fault diagnosis of plunger pump based on audio signal combined with meta-transfer learning

To solve the problems of insufficient samples and weak fault features of audio signals in the fault diagnosis of plunger pump, this paper proposes a fault diagnosis method of plunger pump based on audio signal combined with meta-transfer learning (MTL-PAFD). The method takes the audio signals of the plunger pump as samples, which are acquired by a single sensor. Through the Gammatone filter bank processing, the representation ability of the audio signal under strong noise interference is effectively improved. Then combined with meta-transfer learning, the few-shot fault diagnosis of plunger pump is realized. In addition, according to the actual needs of fault diagnosis of plunger pump, the test method of meta-transfer learning in fault diagnosis application is improved, which can process unknown fault classes adaptively. Experimental results show that MTL-PAFD has a fault diagnosis accuracy of 91.41% for seen classes. After fast adaptive learning, it can achieve an accuracy of 89.64% when identifying unseen fault classes.

An ECT-LSLDA based fault diagnosis method for marine current turbines under variable marine conditions

The development of marine current energy productions has recently gained attention. However, the variation in marine current will lead to weak fault feature and fault misclassification of marine current turbines (MCTs). This paper proposes a new method called extended Concordia transform-latitudinal summation linear discriminant analysis (ECT-LSLDA) to alleviate this problem. First, the extended Concordia transform (ECT) method projects the acquired three-phase currents into the stationary coordinate system to achieve the derivative modulus signals as an effective fault feature. Then, the latitudinal summation linear discriminant analysis (LSLDA) method normalizes and classifies the modulus to realize MCT faults recognition. Experimental results show that the proposed fault diagnosis method achieves high accuracy compare with the state of the art.

A hybrid method of frequency-weighted energy operator and power spectrum fusion to detect bearing faults

The difficulties in early fault diagnosis of bearings mainly include two aspects: first, the initial damage size of the bearing is small, and the abnormal vibration caused by slight damage to the bearing is very weak. Second, vibration signals collected in actual industrial environments always contain strong noise interference. Therefore, traditional diagnostic procedures are not satisfactory. To address these challenges, this work provides a hybrid model combining frequency-weighted energy operator (FWEO) with power spectrum fusion (PSF) to identify weak fault features of bearings and detect different fault types. Different from traditional time-domain signal filtering, PSF is first used to reduce the interference of noise components in the power spectrum, which will not weaken the fault signal components during denoising. Second, the filtered signal is transformed into the time domain and FWEO is employed to further enhance the cyclic fault signal caused by the weak defect of the bearing. Finally, the existence of a fault is identified by observing the squared envelope spectrum of the signal. The effectiveness of the proposed hybrid model is demonstrated through two simulated fault signals and three different experimental fault signals. The results show that the proposed model has high anti-noise performance and robustness and can extract the fault frequency well.

Stochastic Resonance with Parameter Estimation for Enhancing Unknown Compound Fault Detection of Bearings.

Although stochastic resonance (SR) has been widely used to enhance weak fault signatures in machinery and has obtained remarkable achievements in engineering application, the parameter optimization of the existing SR-based methods requires the quantification indicators dependent on prior knowledge of the defects to be detected; for example, the widely used signal-to-noise ratio easily results in a false SR and decreases the detection performance of SR further. These indicators dependent on prior knowledge would not be suitable for real-world fault diagnosis of machinery where their structure parameters are unknown or are not able to be obtained. Therefore, it is necessary for us to design a type of SR method with parameter estimation, and such a method can estimate these parameters of SR adaptively by virtue of the signals to be processed or detected in place of the prior knowledge of the machinery. In this method, the triggered SR condition in second-order nonlinear systems and the synergic relationship among weak periodic signals, background noise and nonlinear systems can be considered to decide parameter estimation for enhancing unknown weak fault characteristics of machinery. Bearing fault experiments were performed to demonstrate the feasibility of the proposed method. The experimental results indicate that the proposed method is able to enhance weak fault characteristics and diagnose weak compound faults of bearings at an early stage without prior knowledge and any quantification indicators, and it presents the same detection performance as the SR methods based on prior knowledge. Furthermore, the proposed method is more simple and less time-consuming than other SR methods based on prior knowledge where a large number of parameters need to be optimized. Moreover, the proposed method is superior to the fast kurtogram method for early fault detection of bearings.

Cycle kurtosis entropy guided symplectic geometry mode decomposition for detecting faults in rotating machinery

Strong noise interference or compound fault coupling phenomenon may lead to the failure of fault diagnosis technology. This paper focuses on weak feature extraction and compound faults detection for rotating machinery fault diagnosis and proposes adaptive symplectic geometric mode decomposition (SGMD) using cycle kurtosis entropy. Firstly, an index named cycle kurtosis entropy (CKE) is presented to measure the strength of periodic impulses in a signal. The CKE uses the entropy value of calculating all delay cycle kurtosis (CK) to overcome the shortcomings of the CK in adaptive ability and obtain more stable values. Thirdly, CKE is applied to construct an adaptive slip window with optimal length. This process is called the adaptive window segmentation method, which is mainly used to dig out weak fault features in signals. Finally, CKE is used as the component selection criterion to select the components decomposed by SGMD. The selected components are reconstructed to obtain a de-noised signal. Hilbert envelope analysis is applied to the denoised signal to demodulate the fault characteristic frequency. Numerical simulations and experimental investigations using bearings and gears are performed to testify the property of the presented method. The results indicate that the adaptive slip window can enhance the decomposing ability of SGMD under strong noise condition. Moreover, for the strong periodic impulse identification ability, the cycle kurtosis entropy is suitable to determine the optimal components of SGMD. It is expected that the presented method will be effectively used for fault feature extractions in rotating machinery under stationary running conditions.