Vibration Based Centrifugal Pump Fault Diagnosis Based on Modulation Signal Bispectrum Analysis

This paper characterises vibration signals using modulation signal bispectrum method in order to develop an effective and reliable feature sets for detecting and diagnosing faults from both the bearings and impellers in a centrifugal pump. As vibration signals contain high level background noises due to inevitable flow cavitation and turbulences, effective noise reduction and reliable feature extraction are critical procedures in vibration signal analysis. Considering the modulation effect between rotating shaft and vane passing components, a modulation signal bispectrum (MSB) method is employed to extract these deterministic characteristics of modulating components in a low frequency band for diagnosing both the bearing defects and impeller blockages. Experimental results show that the diagnostic features developed by MSB allow impellers with inlet vane damages and bearing outer-race faults to be identified under different operating conditions. Not only does this new method produces reliable diagnostic results but also it needs a bandwidth about 1000Hz, rather than the high frequency bands around 10kHz used by conventional envelope analysis

Incipient fault diagnosis of roller bearings using empirical mode decomposition and correlation coefficient

In this paper, an incipient fault diagnosis method for roller bearings is proposed, based on empirical mode decomposition (EMD) and correlation coefficient (the normalized value of the cross-correlation function at the zero-lag point). The high frequency resonance phenomena begin to emerge as a defect forms gradually in the bearings. Therefore the high frequency content is sensitive to the change of the bearing conditions. Based on this, the EMD method is firstly applied to the bearing vibration signals to obtain some intrinsic mode functions (IMF) which contain different frequency bands from high to low. The first IMF of the signal to be detected, representing the high frequency band, is then selected to calculate the correlation coefficient between its frequency-domain signal and that of normal state. The correlation coefficient can demonstrate the fault evolution process and thus can detect an early fault. Finally the early faulty signals are analyzed by using the envelope analysis and the location of the fault is identified. The experimental results verify the effectiveness of the proposed method

A novel faults detection method for rolling bearing based on RCMDE and ISVM

The rolling bearing is an essential element widely used in the rotating machinery. Bearing failures are among the main reasons for breakdown of rotating machinery. Therefore, fault detection of bearing is necessary to reduce the probability of breakdown and safety accidents. A novel fault diagnosis method for rolling bearing based on Refined Composite Multiscale Dispersion Entropy (RCMDE) and Improved Support Vector Machine (ISVM) is presented in this paper. The RCMDE is a new irregular index in biomedical signal analysis, which has lower computational cost and more stable results. Therefore, the RCMDE is introduced as fault feature to represent the bearing fault characteristics. After feature extraction, an improved support vector machine based on whale optimization algorithm (WOA) and support vector machine (SVM) is proposed as a fault classifier, which has the advantages of less training samples and good classification effect. The effectiveness of the proposed method in bearing fault diagnosis is verified by using bearing fault experimental data

Novel complete ensemble EMD with adaptive noise-based hybrid filtering for rolling bearing fault diagnosis

A feature extraction of fault bearing has attracted considerable attention in recent years. However, weak fault feature is difficult to extract under heavy background noise. To solve this problem, a novel multi-layer filtering method is proposed to filter out noise and extract weak fault feature. The first layer introduces a metric based on de-trended fluctuation analysis (DFA) to identify intrinsic mode function (IMF) that reflect period impulsive information for vibration signal adaptively. The second layer uses non-local mean (NLM) method as a pre-filter of the third layer to realize extraction of singular value decomposition (SVD) which reflect the most information of IMFs. The last layer introduces a relative energy difference criterion of a singular value to extract important feature of Hankel matrix of IMFs. The filtered signal is obtained by re-constructed signal from identified singular value of SVD. Experiment results on simulation and real vibration signals indicate that the hybrid filtering method removes heavy noise successfully and extract weak fault feature of rolling bearing effectively

Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP model

Rolling bearings are one of the most widely used bearings in industrial machines. Deterioration in the condition of rolling bearings can result in the total failure of rotating machinery. AI-based methods are widely applied in the diagnosis of rolling bearings. Hybrid NN-based methods have been shown to achieve the best diagnosis results. Typically, raw data is generated from accelerometers mounted on the machine housing. However, the diagnostic utility of each signal is highly dependent on the location of the corresponding accelerometer. This paper proposes a novel hybrid CNN-MLP model-based diagnostic method which combines mixed input to perform rolling bearing diagnostics. The method successfully detects and localizes bearing defects using acceleration data from a shaft-mounted wireless acceleration sensor. The experimental results show that the hybrid model is superior to the CNN and MLP models operating separately, and can deliver a high detection accuracy of 99,6% for the bearing faults compared to 98% for CNN and 81% for MLP models

Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal

A method of planetary gear fault diagnosis based on the fuzzy entropy of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-layer perceptron (MLP) neural network is proposed. The vibration signal is decomposed into multiple intrinsic mode functions (IMFs) by CEEMDAN, and the fuzzy entropy that combines the fuzzy function and sample entropy is proposed and used to extract the feature information contained in each IMF. The fuzzy entropies of each IMF are defined as the input of the MLP neural network, and the planetary gear status can be recognized by the output. The experiments prove the proposed method is effective

A feature extraction method based on ICD and MSE for gearbox

Since the vibration signals of gearbox are non-linear and non-stationary, it is difficult to accurately evaluate the working conditions. Therefore, a fault feature extraction technique based on intrinsic characteristic-scale decomposition (ICD) and multi-scale entropy (MSE) is presented in this paper. The measured signals are firstly decomposed into a series of product components (PCs) by ICD. Secondly, the main product component is selected, and then MSE is used to extract the feature vectors from the selected PCs. Finally, the obtained feature vectors of gearbox with different scale factors are adopted as inputs of support vector machine (SVM) to fulfill the fault patterns identification. The superiority of the proposed technique is verified through comparing with three other methods