Measured Vibration Signals Research Articles

Signal Identification of Gear Vibration in Engine-Gearbox Systems Based on Auto-Regression and Optimized Resonance-Based Signal Sparse Decomposition

As an essential part of the transmission system, gearboxes are considered as a major source of vibration. Signal identification of gear vibration is necessary for online monitoring of the mechanical systems. However, in engine-gearbox systems, the ignition impact of the engine is strong, so that the gear vibration is generally submerged. To overcome this issue, the resonance-based signal sparse decomposition (RSSD) method is used in this paper based on different oscillatory behaviors of the gear meshing impact and the engine ignition impact. To improve the accuracy of RSSD under interferences, the meshing frequency energy ratio (MF–ER) index is introduced into RSSD to adaptively choose the decomposition parameters. Before applying the RSSD method, the auto-regression (AR) model is used as a pre-whitening step to eliminate the normal gear meshing vibration, which improves the decomposition performance of RSSD. The effectiveness of the proposed AR-ORSSD (AR-based optimized RSSD) algorithm is tested using both simulated signals and measured vibration signals from an engine-gearbox system in a forklift. Comparisons were made with the RSSD algorithm based on a genetic algorithm. Experimental results indicate that the AR-ORSSD algorithm is superior at identifying gear vibration signals especially when under strong interferences.

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet.

Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust.

Applications of vibro-acoustic measurement and analysis in conjunction with tribological parameters to assess surface fatigue wear developed in the roller-bearing system

Rolling-element bearings are the most commonly used components in all rotating machinery. The variations in the operating conditions such as an increase in the number of operating cycles, load, speed, service temperature, and lubricant degradation result in the development of various defects such as pitting, spalling, scuffing, scoring, etc. The defects that appeared on rolling contact surfaces cause surface deterioration and change in the vibration and sound levels of the bearing system. The present experimental investigations are aimed at assessing the surface fatigue wear that appears on the contact surfaces of roller bearings. The studies considered the estimation of specific film thickness, analysis of surface fatigue wear developed on the rolling-element surfaces, surface roughness analysis, grease degradation analysis using Fourier transform infrared radiation, and vibration and sound signal measurement and analysis. The results obtained from the experimental investigation provide a good correlation between surface wear, vibration, and sound signals with a transition in the lubrication regimes in the Stribeck curve.

3D modelling and visualization for Vision-based Vibration Signal Processing and Measurement

Abstract With the technological evolutionary advent, a vision-based approach presents the remote measuring approach for the analysis of vibration. The structure vibration test and model parameter identification in the detection of the structure of the bridge evaluation occupies the important position. The bridge structure to operate safely and reliably is ensured, according to the geological data of qixiashan lead-zinc mine and engineering actual situation, with the aid of international mining software Surpac. To build the 3D visualization model of the application of visualization in mine production are discussed. The results show that the final solid model of -425 stope can accurately display the spatial form of each layer of stope through rotation, amplification and movement. The proposed system is effectually able to perform cutting, volume calculation and roaming in any direction, which has certain guiding significance for mine production management. An accuracy value of 98.75%, the sensitivity of 99%, specificity of 99.64% and PPV of 99.89% are achieved using the proposed 3D modelling and visualization algorithm for vibration signal processing and management.

Analysis and control of the motor vibration using arduino and machine learning model

The motor vibrations provide information for diagnosing and predicting errors through signal processing. Motor vibrations provide information for diagnosing and predicting errors through data signal. In this article, we introduce an IoT-based detecting scheme for recording the vibration of an induction motor. A three different sensor such as vibration, Micro-Electro-Mechanical Systems (MEMS) and temperature sensor is used to gather the data from the motor vibration. We conducted an experiment by analysing the motor vibration by using Arduino, when the motor vibration is going to abnormal at that time Arduino send the signal to relay by cutting the supply to the motor. However, the motor running on proper condition with proper temperature, the controller is continuously monitoring and forward the data to storage system. The results can presentation on the mobile phone. The experiments were performed in the steady-state condition and the measured vibration signals were analysed using the Discrete Fourier Transform (DFT). Depend on the outcomes of the proposed model, which greatly monitor and early diagnosis the motor vibration, and performance was analysed by using the Machine Learning model of Decision Tree (DT). The controller can forward the vibration data the cloud with a maximum delay of about 0.9 sec. the stored data is retrieved by using the train the DT model to analysis the performance classification accuracy. This article introduces a new method for implementing real-time vibration measurement and analysis tools based on MATLAB.

Lkurtogram Guided Adaptive Empirical Wavelet Transform and Purified Instantaneous Energy Operation for Fault Diagnosis of Wind Turbine Bearing

The periodic impacts are regarded as the typical characteristics of local defect of wind turbine bearing. For this reason, it is significant to extract the periodic impacts from the original measured vibration signal with background noise interferences during the defect identification process. For the purpose of effectively solving this issue, an innovative diagnostic frame based on Lkurtogram guided adaptive empirical wavelet transform (LGAEWT) and purified instantaneous energy operation (PIEO) is put forward. Within this diagnostic frame, the L-kurtosis indicator guided wavelet equivalent filter band mergence is carried out to confirm the optimal filter boundary, then the resonance frequency band containing rich characteristic information can be extracted, and the characteristic sensitive component is able to be separated from the original vibration signal. In addition, a novel signal processing strategy called PIEO is presented to inhibit the background interferences and enhance the periodic impact signatures in sensitive components, and the purified instantaneous energy spectrum is adopted to replace the traditional envelope spectrum for characteristic frequency spectral lines identification. The feasibility of the proposed diagnostic frame has been demonstrated by the experimental signals and the actual engineering case, and the results manifest that it is suitable for bearing fault enhance detection under low signal-to-noise ratio (SNR) environment. Furthermore, the comparison results with the widely used Kurtogram, Autogram, and DWT methods indicate that this innovative diagnostic strategy has more prominent advantages on background interference suppression and weak characteristic intensification.

Appraisal of gear pitting severity by vibration signal analysis

Gears are mechanical components used to transfer power between two machines and to modify the operating speed. When they are operating at high loads and speeds the gear teeth surfaces are subjected to loads, which, even in proper lubrication conditions, are leading to failures like scuffing, scoring, spalling or pitting. As the vibration of a gearbox is carrying the signature of the gear faults, these deficiencies may be detected by measuring and analysing the vibration signal. In this research, experimental investigations were accomplished in order to evaluate the gear pitting severity by means of vibration signal measurement and analysis. Thus, four pinions with different pitting grades, created by artificial means, where incorporated in a single helical gearbox and tested on an open-energy test rig. The collected vibration measurements have provided the information regarding the pitting severity, supplying information for the assessment of the gears failure stage. For processing the signals a relative new technique was used, namely the Sideband Energy Ratio™ (SER).

An automatic speed adaption neural network model for planetary gearbox fault diagnosis

In real-world applications, fault detection and diagnosis of planetary gearboxes are vital if it can be employed to avert catastrophic failure consequences in rotating machinery. Fault diagnosis usually starts with collecting vibration signals from rotating machinery. These vibration signals are usually produced in non-stationary operating conditions with time-varying loads and speeds, which makes fault diagnosis more challenging. Signal processing methods are typically selected for fault diagnosis to capture either time, frequency, or time-frequency based diagnostic features from measured vibration signals. Yet, it is usually a costly or time-consuming process and, sometimes, heavily dependent on human expertise. Although current deep learning algorithms offer an efficient and intelligent diagnostic strategy for fault diagnosis, unfortunately, most of the reported algorithms are basically only valid for the stationary operating conditions. To address the challenges of non-stationary operating conditions, in this paper, an Automatic Speed Adaption Neural Network (ASANN) model within the incorporation of instantaneous rotating speed is proposed, and it provides an end-to-end learning fashion with the guidance of rotating speed information. With the incorporating of instantaneous rotating speed information, the proposed ASANN model enables the extraordinary capacity for planetary gearbox fault detection under varying operational scenarios. The validity of the ASANN model is verified by an experimental investigation of fault diagnosis in a planetary gearbox.

Research on Vibration Signal Analysis Method of Transformer Winding Based on Wavelet Packet Transform and LMD Algorithm

Power transformer is an important equipment in power system, and its operation status has an important impact on the safe and stable operation of power system. However, most of the existing methods are off-line detection, which can not meet the requirements of engineering practice. Therefore, how to monitor and analyze the operation status of transformer based on the on-line measured vibration signal has become a research hotspot in this field. Therefore, this paper introduces a wavelet packet transform and local mean decomposition (LMD) decomposition method based on EMD. In this method, wavelet packet transform is used to denoise the vibration signal of transformer winding, and then it is adaptively decomposed into a series of product function (PF) components by LMD, so as to reveal the signal characteristics and realize on-line monitoring and analysis of transformer winding. Finally, the feasibility and effectiveness of the analysis method in this paper is verified by combining theory with simulation.

Designing Hand-Held Vibration Measuring Device For Industrial Machines

Evaluating the quality of industrial machines, the vibration meter is used to measure the machine’s actual vibration. The two most important parameters describing machine vibration, amplitude, and frequency, are the key factors for determining the cause of vibrations. Spectral analysis of the vibration signal will give information about the vibration level and choose which part of the machine leads to the observed vibration signal. This paper presents a self-made machine vibration measuring device with a simple structure, compact size, high accuracy at a reasonable price. The device can display the measured vibration signals’ real-time value and analyze the computer’s spectral to produce the required parameters.

Synchrosqueezing extracting transform and its application in bearing fault diagnosis under non-stationary conditions

The measured vibration signals of mechanical equipment with defects are generally non-stationary. Time–frequency analysis is an effective tool in processing non-stationary signals. However, the concentration of time–frequency representation will greatly affect the performance of feature extraction. Thus, a more concentrated time–frequency analysis method plays an important role in mechanical signal processing and fault diagnosis. A novel time–frequency analysis method, called synchrosqueezing extracting transform has been proposed in this study. The proposed method can not only well extract the time-varying information of the nonstationary signal then achieve a better-concentrated time–frequency representation, but also have better noise robustness and lower time-consuming than the traditional time–frequency analysis methods. The analysis of numerical signals verified the great performance of this method. Finally, synchrosqueezing extracting transform is used to process the bearing vibration signals under variable speed conditions. It has been demonstrated that the proposed method achieves better results than other state-of-art time–frequency analysis methods.

Method of S Wave Identification: Application to Measured blasting vibration signals

S wave identification is an indispensable step in the process of deriving the dynamic parameters of rock masses, which are highly significant in guiding the design and construction of hydraulic engineering projects. However, it is difficult to identify S waves accurately because of 1) the influence of overlapping within the P wave coda and 2) the incomplete separation between S and P waves owing to the characteristic short distances of blasting seismic wave propagation. This paper presents a modified method for accurately identifying S waves at the engineering scale using blasting vibration signals. The proposed method combines the application of the short-time average zero-over rate with polarization analysis. By using the short time average zero-over rate, it is possible to effectively reduce the computational time while improving the computational efficiency. Polarization analysis is used to improve the accuracy of the method. A comparison of numerical identification and theoretical results revealed that the improved method is clearly capable of S wave identification with errors of less than 2%. To demonstrate the capability and accuracy of the proposed method, measured vibration signals were introduced to the Fengning pumped-storage power station and the S wave velocities obtained from the first arrival times were compared to obtain an estimated S wave velocity.

A fault pattern recognition method for rolling bearing based on CELMDAN and fuzzy entropy

The vibration signal of rolling bearing often has the characteristics of strong noise, nonlinearity and non-stationary, so the accurate fault feature information cannot be obtained directly from the measured vibration signal. Therefore, a fault pattern recognition method for rolling bearing based on complete ensemble local mean decomposition with adaptive noise (CELMDAN) and fuzzy entropy is deeply studied. Firstly, the reason of modal aliasing existing in local mean decomposition (LMD) method is explained. According to the previous methods for modal aliasing processed in other methods, CELMDAN method is proposed. The experiment proves that the proposed CELMDAN method can better handle the vibration signals with nonlinear and non-stationary. Then, the principle and properties of the fuzzy entropy are introduced in detail, and the fault feature of rolling bearing can be reflected. Finally, extreme learning machine (ELM) is introduced as the pattern recognition method based on the effective fault feature of rolling bearing. Combined with the verification of experimental signal, it is proved that the proposed method can extract the fault features of rolling bearing accurately and effectively, and the fault pattern recognition of rolling bearing can be realized.

Multi-label fault diagnosis of rolling bearing based on meta-learning

In practical applications, it is difficult to acquire sufficient fault samples for training deep learning fault diagnosis model of rolling bearing. Aiming at the few-shot issue and multi-label attributes of single-point faults, a novel fault diagnosis method of rolling bearing based on time–frequency signature matrix (T–FSM) feature and multi-label convolutional neural network with meta-learning (MLCML) is proposed in this paper. At the beginning, the T–FSM features sensitive to few-shot fault diagnosis of measured vibration signal are extracted. Subsequently, a designed multi-label convolutional neural network (MLCNN) with a specific architecture is employed to identify faults. Crucially, the meta-learning strategy of learning initial network parameters susceptive to task changes is incorporated to MLCNN for addressing the few-shot problem. Ultimately, the publicly available rolling bearing dataset is utilized to demonstrate the effectiveness of the proposed method. The experimental results exhibit that the trained MLCML has the capability of learning to learn few-shot fault attributes with outstanding diagnosis accuracy and generalization. More concretely, the model can adapt to new fault categories rapidly owing to that only a few samples and update steps are required to fine-tune the network.

A fault diagnosis method of rolling bearing based on VMD Tsallis entropy and FCM clustering

A new fault diagnosis method of rolling bearings was presented based on variational mode decomposition (VMD), Tsallis entropy and Fuzzy C-means clustering (FCM) algorithm. Firstly, the measured vibration signals were decomposed with VMD in different scales to obtain a series of band-limited intrinsic modal function (BIMF). The VMD parameters were determined according to the change of the BIMF center frequency. Then, the Tsallis entropy of BIMF components were calculated and used as the signal features. Finally, the features were put into FCM classifier to recognize different fault types. It is proved by experiments that this method is feasible and the proposed approach could obtain better result compared with the method based on mode decomposition (EMD) and local mean decomposition (LMD).

Mechanical Fault Diagnosis of Circuit Breakers Based on XGBoost and Time-domain Features

In order to improve the efficiency of feature extraction of mechanical vibration signal of circuit breaker and the reliability of state recognition of circuit breakers, a mechanical fault diagnosis method of high voltage circuit breaker based on XGBoost is adopted. Firstly, 17 time-domain features are extracted from the measured vibration signals of circuit breakers, constructing feature vector and the separability of eigenvectors is analyzed. Then the feature vector is input into XGBoost, the depth and size of the tree are optimized to realize the high reliability discriminant analysis of the mechanical state of circuit breaker. Experiments on vibration data of circuit breakers prove that, this method has high efficiency in feature extraction and the overall recognition accuracy is high.

A smart sensor-based monitoring system for vibration measurement and bearing fault detection

Rolling element bearings are commonly used in rotary mechanical and electrical equipment. According to investigation, more than half of rotating machinery defects are related to bearing faults. However, reliable bearing fault detection still remains a challenging task, especially in industrial applications. The objective of this work is to develop a smart sensor-based monitoring system for vibration measurement and bearing fault detection. In this work, a smart sensor data acquisition (DAQ) system is developed for vibration signal measurement. A selective Teager–Huang transform (THT) technique is proposed for bearing fault detection; it is comprised of three processes: Firstly, a denoising filter is used to improve the signal-to-noise ratio; secondly, a correlation function is suggested to choose the most representative intrinsic mode functions (IMFs); and thirdly, a generalized Teager–Huang spectrum method is proposed to process the extracted IMFs for bearing fault detection. The effectiveness of the developed DAQ system and selective THT technique is verified by experimental tests.

Compound fault diagnosis of rolling element bearings using multipoint sparsity–multipoint optimal minimum entropy deconvolution adjustment and adaptive resonance-based signal sparse decomposition

The rolling element bearings used in rotating machinery generally include multiple coexisting defects. However, individual defect–induced signals of bearings simultaneously arising from multiple defects are difficult to extract from measured vibration signals because the impulse-like fault signals are very weak, and the vibration signal is commonly affected by the transmission path and various sources of interference. This issue is addressed in this study by proposing a new compound fault feature extraction scheme. Vibration signals are first preprocessed using resonance-based signal sparse decomposition to obtain the low-resonance component of the signal, which contains the information related to the transient fault–induced impulse signals, and reduce the interference of discrete harmonic signal components and noise. The objective used for adaptively selecting the optimal resonance-based signal sparse decomposition parameters adopts the ratio of permutation entropy to the frequency domain kurtosis, as a new comprehensive index, and the optimization is conducted using the cuckoo search algorithm. Subsequently, we apply multipoint sparsity to the low-resonance component to automatically determine the possible number of impulse signals and their periods according to the peak multipoint sparsity values. This enables the targeted extraction and isolation of fault-induced impulse signal features by multipoint optimal minimum entropy deconvolution adjustment. Finally, the envelope spectrum of the filtered signal is used to identify the individual faults. The effectiveness of the proposed scheme is verified by its application to both simulated and experimental compound bearing fault vibration signals with strong interference, and its advantages are confirmed by comparisons of the results with those of an existing state-of-the-art method.

Intelligent fault identification of rotary machinery using refined composite multi-scale Lempel–Ziv complexity

Fault diagnosis of rotary machinery plays a significant role in the prognostic and health management system, which aims to identify the root causes of system failures and provide effective information for prognostics and maintenance. Recently, Lempel–Ziv complexity (LZC) method has been employed for fault diagnosis of rotary machinery. However, one actual problem is that LZC fails to account for the multiscale information inherent in measured vibration signals. We first introduce a method to compute the multi-scale LZC for a signal. However, the variance of LZC values becomes larger as the scale factor increases. To solve this actual problem, this paper proposes refined composite multi-scale Lempel-Ziv complexity (RCMLZC) to estimate the complexity. We find that the proposed RCMLZC method consistently yields better performance when analyzing three simulated noisy and impulsive signals. Based on RCMLZC, a novel intelligent fault diagnosis method is designed to recognize various fault types of rotating machinery. Comparative experiments are performed to confirm the effectiveness of proposed method including single fault and compound fault working conditions. Experimental results indicate that RCMLZC is more accurate than multi-scale LZC, multi-scale entropy, and LZC in extracting fault features from vibration signal and that RCMLZC performs best to recognize the various fault types of rotary machinery.

Compound fault diagnosis for a rolling bearing using adaptive DTCWPT with higher order spectra

Fault diagnosis plays a vital role in prognostics and health management. Researchers have devoted their efforts in enhancing the accuracy of fault diagnosis. However, diagnosis of compound faults in complex systems is still a challenging task. The problem lies in the coupling of multiple signals, which may conceal the characteristics of compound faults. Taking a rolling bearing as an example, this study aims to boost the accuracy of compound fault diagnosis through a novel feature extraction approach to making the fault characteristics more discriminative. The approach proposes an adaptive dual-tree complex wavelet packet transform (DTCWPT) with higher order spectra analysis. To flexibly and best match the characteristics of the measured vibration signals under analysis, DTCWPT is first adaptively determined by the minimum singular value decomposition entropy. Then, higher order spectra analysis is performed on the decomposed frequency sensitive band for feature extraction and enhancement. The proposed approach is used to analyze experimental signals of a bearing’s compound faults and found effective.