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| Authors | Methodologies | Findings |
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| [63] | Incorporated the RMS and kurtosis values in NN to diagnose the rolling element bearings fault | The proposed method can effectively diagnose the condition using only a few features of vibration data but the fault types and severity level cannot be determined |
| [71] | Applied the kurtosis and SVM method to diagnose roller bearing fault | The accuracy of the proposed method is 93.75% and can be applied even with a limited number of samples |
| [23] | Used the FFT and PSD methods to monitor the condition of the machine | A PC-based vibration analyzer, incorporating the proposed method was developed |
| [73] | Applied the FFT method to diagnose induction motor fault | The simulation results obtained from MATLAB/Simulink are in agreement with the experimental results |
| [78] | Combined the cepstrum analysis and NN method to detect and diagnose gear fault | NN can diagnose gear faults with high accuracy, provided that proper measured data are used |
| [79] | Used two cepstrum analysis approaches, namely, automated cepstrum editing procedure (ACEP) and cepstrum prewhitening (CPW) to detect bearing fault | CPW approach is more suitable for applications that do not require bandpass filtering but applying both approaches without prior knowledge can lead to false result in detecting bearing faults |
| [81] | Applied the envelope analysis to diagnose faults in rotating machines under variable speed conditions | Squared envelope method is an optimal approach in fault diagnosis in terms of computational cost and simplicity compared to the improved synchronous average (ISA), the cepstrum prewhitening (CPW), and the generalized synchronous average (GSA) |
| [86] | Used the envelope analysis to diagnose bearing faults | The squared envelope method is more suitable to analyze the cyclostationary signals compared to the envelope method |
| [90] | Combined the higher-order spectrum analysis and SVM to diagnose faults in power electronic circuit | The proposed method achieved the accuracy of up to 99% |
| [91] | Applied the power spectrum analysis (PSA) and SVM to diagnose rolling bearing fault | Using the PSA with SVM classifier gives better result compared to NN classifier |
| [100] | Applied the WPT method to monitor the condition of the machine | Using the proposed method as input to a NN classifier produced nearly 100% classification accuracy. Also, the proposed method produced a better result compared to FFT when the data are corrupted by noise |
| [102] | Combined the Hilbert transform and WPT to detect gearbox fault | The proposed method is capable of detecting early gar fault |
| [101] | Applied a denoising method based on WT to diagnose rolling bearing and gearbox | The proposed method is more effective and has more advantages compared to Donoho’s soft-thresholding denoising method |
| [103] | Proposed an orthonomal DWT (ODWT) method to monitor and diagnose bearing faults at an early stage | The proposed method outperforms the EEMD and Hilbert envelope spectrum analysis method |
| [115] | Applied the HHT and FT methods to diagnose machine fault | HHT outperforms FT, where FT can only differentiate characteristic frequency in low-frequency band |
| [116] | Proposed a new local mean parameter to improve the HHT method to detect gearbox fault | Introducing the new parameter improves the HHT process and makes the fault detection process simpler |
| [120] | Applied the STFT method to diagnose faults in a hydroelectric machine | The basis for the effective fault diagnosis of hydroelectric machines was proposed |
| [121] | Combined STFT and SVM methods to diagnose faults in induction motor | The proposed method has a huge potential to diagnose fault intelligently in other real-time system applications |
| [122] | Combined the STFT and nonnegative matrix factorization (NMF) methods to detect rolling bearing element fault | The proposed method is able to determine the fault types and severity and yields 99.3% accuracy, superior to NN |
| [125] | Applied the PSD method to diagnose Massey Ferguson gearbox | The proposed method can reliably and quickly diagnose gearbox fault |
| [126] | Used the PSD and SVM methods to diagnose gearbox faults | The proposed method achieved an accuracy of 98.82% and 97.16% for spur and helical gearbox dataset, respectively |
| [133] | Used the SVM method to diagnose rolling bearing element fault based on the fractal dimensions | SVM trained with 11 time domain statistical features and three fractal dimensions provides better results compared to the SVM trained with only fractal dimensions or with time domain statistical features |
| [134] | Applied the SVM, K-nearest neighbor (KNN), and Fischer linear discriminant (FLD) methods to diagnose engine fault | The performance of the SVM method is much better compared to the KNN and FLD method |
| [135] | Presented a real-time online monitoring approach for the disc slitting machine based on WPT and SVM methods | Findings show that different types of disc slitting machines’ faults can be successfully detected with an accuracy of 95.6% |
| [64] | RMS values were among the statistical features employed as input parameters for the DNN to monitor the condition of gearbox | It was found that the deep learning methods are superior compared to the NN method, which has low robustness in diagnosing the condition of gearbox |
| [145] | Compared the combination of GA with three types of NN, namely, multilayer perceptron (MLP), radial basis function (RBF), and probabilistic neural network (PNN) to detect bearing fault | The combination of GA with MLP and PNN gave 100% success rate whereas RBF gave 99.31% rate |
| [146] | Applied the combination of EMD and NN methods to diagnose the roller bearing fault | The proposed method can successfully diagnose roller bearing fault but has an end effect complication |
| [147] | Combined the WPT, GA, NN, and SVM methods to diagnose fault in diesel engine | The proposed method produced 100% classification accuracy |
| [148] | Proposed a CNN approach that makes use of cyclic spectrum maps (CSMs) of raw vibration signal to diagnose the motor bearing in the rotating machine | Based on the validation with benchmark vibration data collected from bearing tests, the proposed technique is superior to its referenced methods in terms of the classification accuracy |
| [149] | Proposed a distribution-invariant deep belief network (DIDBN) as a basis for intelligent fault diagnosis of machines | The proposed method is able to achieve a high diagnosis accuracy even with new working conditions |
| [150] | Used the CNN method with 1D image of raw three-axis accelerometer signal as the input | It was found that CNN trained with a higher number of kernels in the first layer produced slightly better performance |
| [151] | Proposed a hybrid deep signal processing method to diagnose bearing faults in the machine, where the signal processing, feature extraction, and bearing fault diagnosis were automatically conducted | The proposed method is superior to the manual extraction methods and commonly used deep learning structures in terms of accuracy, and it is not affected by the operation conditions |
| [152] | Presented the augmented deep sparse autoencoder (ADSAE) method in diagnosing gear faults, where data shifting technique was incorporated to enhance the SAE model | Compared with other deep learning architectures, the proposed method provides a higher accuracy (99%) and only requires a few raw vibration signal data |
| [62] | Combined the decision tree and fuzzy logic methods to diagnose spur gear fault based on the statistical features such as RMS, crest factor, and kurtosis | The performance of the proposed method in diagnosing fault was found to be 95% |
| [160] | Proposed the fuzzy logic method to diagnose the operation of rotating machines | The proposed method can easily diagnose the operational status of the rotating system |
| [161] | Applied the fuzzy logic method to monitor and diagnose the condition of the pump | The proposed method can successfully identify and classify the faults of the five-plunger pump |
| [162] | Proposed the combination of DWT and fuzzy logic to predict the presence of misalignment in rotating machinery | The proposed approach has an error of less than 1% in predicting the degree of misalignment |
| [163] | Developed a gas turbine vibration monitoring approach based on Takagi–Sugeno fuzzy logic | Experts’ knowledge regarding the maintenance of the gas turbine in accordance to the vibration level detected can be successfully expressed by the proposed method |
| [168] | Proposed a combination of wavelet support vector machine (WSVM) and immune genetic algorithm (IGA) to diagnose gearbox fault | The proposed method yielded a better diagnostic accuracy compared to the SVM and NN method, in addition to strong generalization capability |
| [169] | Combined the GA, SVM, and EEMD methods to diagnose gear faults | Incorporating the GA to select the parameter of SVM can improve the generalization ability and classification accuracy of the diagnostic system |
| [170] | Applied the combination of GA and SVM in bearing fault diagnosis | Applying the cross-validation method to optimize SVM outperforms the SVM method optimized by GA in bearing fault diagnosis |
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