Collected Vibration Signals Research Articles

Resilient fast convolutional sparse filtering with convergence directivity for the bearing fault diagnosis

The fast nonlinear convolutional sparse filtering (FNCSF), which detects fault features 42 h earlier than traditional methods in the intelligent maintenance system dataset and takes only 0.12 s, is widely considered a powerful tool for early fault diagnosis. However, random shocks caused by the structure or external interference pose challenges to the extraction accuracy of FNCSF. In addition, the extraction reliability of FNCSF is affected by computational instability and complex parameter settings. To address the above defects, resilient fast convolutional sparse filtering (RFCSF) is proposed in this study. First, the collected vibration signal is normalized by Z-score to eliminate the scale variability of the nonlinear transformation. Then, the frequency components of the signal are evenly divided by the initialized filters to indicate the convergence direction of fault and improve random shock resistance and extraction stability. Next, the nonlinear [Formula: see text] norm, which can effectively distinguish fault features from irrelevant disturbances, is regarded as the target of blind deconvolution. In the process of deconvolution, the filter converging to random shocks and noise is adaptively eliminated to improve the extraction efficiency and intelligence. Finally, the filtered signal is envelope demodulated to obtain fault information. In the simulation analysis, features under noise with −16 dB and random shocks with 50 m/s2 are accurately extracted by RFCSF with parameters robustness. Several experimental cases demonstrate that RFCSF with these advantages is a promising feature extraction tool.

Incipient fault detection for the spindle bearing of a cement grinding machine based on vibrational resonance

Abstract The spindle bearing of a circulation fan is an important component of a cement grinding machine. In addition to the faults on the spindle bearing, impeller wear and ash accumulation may cause dynamic unbalance and complex vibration interference as noise, which decreases the accuracy of fault detection based on vibrational signals and traditional signal processing-based methods at the early stage of a bearing fault. To address this issue, this paper presents a new fault detection method for the spindle bearing by utilizing extra injected noise and vibrational resonance. To enhance the fault signature and resonance performance, the nonlinear system of the traditional vibrational resonance is replaced by a new hybrid steady-state system, and the underdamped term is considered in the new system. The proposed system provides more possibilities to achieve resonance by adjusting the system parameters and overcomes the limitations of output saturation caused by the classical bistable system. The proposed method is validated by analyzing the collected vibration signals from a spindle bearing of a circulation fan in practice and is compared with other noise-elimination fault detection methods. The results demonstrate the effectiveness and superior performance of the proposed method.

Fault diagnosis of the harmonic reducer based on dual-path convolutional network with multi-channel hybrid attention mechanism

Abstract Harmonic reducers are prone to wear and other failures due to continuous deformation of the flexure wheel during operation. To evaluate the sorts of faults in harmonic reducers under various operating situations, a fault diagnosis method (MADCNN-BiGRU) based on dual-path convolution neural network (DCNN) with multi-channel hybrid attention mechanism (MCHAM) and bidirectional gated recurrent unit (BiGRU) was proposed. Firstly, a novel MCHAM focuses on the critical information part of the vibration signal and can effectively suppress the noise and redundant information. Secondly, a new soft threshold function is constructed to regulate the attention weights dynamically. Thirdly, BiGRU obtains feature information for different time series locations to identify the faults correctly. Fourthly, a harmonic reducer test rig is established to collect vibration signals of harmonic reducers with different faults under various operating situations. The comparative experimental results demonstrate that MADCNN-BiGRU has excellent capacity for robustness and generalization, and can effectively diagnose under various operating situations.

Real-time monitoring of aero-engine vibration signals by wireless communication technology

Abstract The conventional way to monitor the vibration signals of aircraft engines is to use wired communication sensors to transmit and collect the signals on a fixed platform on the ground. The signal acquisition equipment of wired communication technology has various drawbacks, resulting in the inability to collect vibration signals when the object under test is in real operating conditions. Based on previous studies, this research team uses a 2.4G wireless transmission scheme to realize short-distance wireless transmission of aero-engine vibration signals, and a 4G cellular communication scheme to realize long-distance wireless transmission of aero-engine vibration signals. The acquired signals are displayed and monitored using the tool developed in LabVIEW2022. The designed tool is successfully applied in the engineering practice of engine maintenance, realizing the remote real-time monitoring of the engine operation status, shortening the monitoring cycle of the equipment from hourly to real-time level, and greatly facilitating the technical support work of engineers and technicians.

Damage detection of Kevlar woven fabric using optical fiber multimode interferometer

Kevlar woven fabric is crucial for the lightweight design of aerospace structures due to its unique advantages. However, it is susceptible to damage during operation, making effective damage detection essential for ensuring structural reliability. Nevertheless, conventional sensors used for damage detection have limitations such as complex manufacturing processes and weight. To address this issue, this paper proposed a simple singlemode-multimode-singlemode (SMS) optical fiber sensor to collect vibration signals and construct a damage recognition model based on natural frequency in order to identify the size of damages in Kevlar woven fabrics. To achieve a highly linear and accurate sensor, a length determination criterion for multimode optical fiber segments based on quasi-image, notch, and transmission spectrum loss was proposed. An optical sensor was fabricated based on this criterion. The feasibility and sensitivity of the sensor for vibration signal measurement were verified by constructing a vibration signal measurement device. An experimental study was conducted to detect damage in Kevlar tapes, where the natural frequency changes measured by the SMS sensor were integrated with an optimized multi-variable gray model for damage size detection. The experimental results indicate that the estimated error in damage size is 0.71%. The damage identification system proposed in this paper offers a simple and cost-effective solution for measuring damage in flexible structures using fiber optic sensors.

Research on Fault Diagnosis of Drilling Pump Fluid End Based on Time-Frequency Analysis and Convolutional Neural Network

Operating in harsh environments, drilling pumps are highly susceptible to failure and challenging to diagnose. To enhance the fault diagnosis accuracy of the drilling pump fluid end and ensure the safety and stability of drilling operations, this paper proposes a fault diagnosis method based on time-frequency analysis and convolutional neural networks. Firstly, continuous wavelet transform (CWT) is used to convert the collected vibration signals into time-frequency diagrams, providing a comprehensive database for fault diagnosis. Next, a SqueezeNet-based fault diagnosis model is developed to identify faults. To validate the effectiveness of the proposed method, fault signals from the fluid end were collected, and fault diagnosis experiments were conducted. The experimental results demonstrated that the proposed method achieved an accuracy of 97.77% in diagnosing nine types of faults at the fluid end, effectively enabling precise fault diagnosis, which is higher than the accuracy of a 1D convolutional neural network by 14.55%. This study offers valuable insights into the fault diagnosis of drilling pumps and other complex equipment.

Diagnosis for railway point machines using novel derivative multi-scale permutation entropy and decision fusion based on vibration signals

Abstract Railway point machines (RPMs) are safety-critical pieces of equipment closely related to train operation safety. Due to their high failure rate, it is urgent to develop an effective diagnosis method for RPMs. Considering the easy-to-collect and anti-interference characteristics of vibration signals, this paper develops a vibration-based diagnosis method. First, to address the difficulty of multi-scale permutation entropy in characterizing the fault information contained in the derivatives of the raw signal, a novel feature named derivative multi-scale permutation entropy is designed, which can further complete the fault information of RPMs. Second, to further improve the diagnosis accuracy of support vector machines, a decision fusion strategy based on three feature sets is developed, which can further improve the diagnosis accuracy, especially in the normal-reverse direction. Finally, the effect and superiority of the proposed method are verified based on the collected vibration signals from Xi’an Railway Signal Co.,Ltd by experiment comparisons. The diagnosis accuracies of reverse-normal and normal-reverse directions reach 99.43% and 100% respectively, indicating its superiority.

Weighted squared envelope dispersion entropy as nonlinear measure for dynamic health monitoring of rotating machineries

In the context of nondestructive testing and evaluation, dispersion entropy (DisE) stands out as a promising dynamic nonlinear health monitoring measure for rotating machineries. However, in high-noise scenarios, transient impulses linked to rotating machinery faults often get submerged under the noise component present in the collected vibration signal. As a result, DisE not only fails to detect the presence of a fault at the earliest stage of inception but also performs poorly in tracking the progression of the incepted fault. Aiming at overcoming the limitations of DisE in dynamic health monitoring of rotating machineries, in this paper, impulses corresponding to a fault is extracted by suppressing the unnecessary noise component by weighting the squared envelope of the collected vibration signal. Due to the application of weighted squared envelope in calculating the DisE, the proposed measure is termed as weighted squared envelope dispersion entropy (WSEDisE). Effectiveness of WSEDisE in dynamic health monitoring of rotating machineries is verified by two different experimental run to failure data collected from rolling element bearings and spur gears. Experimental results show that WSEDisE not only overcomes the weaknesses of original DisE but also demonstrates better performance than conventional entropy-based methods such as permutation entropy (PE) and advanced DisE based method namely multiscale DisE (MDisE).

Remaining useful life prediction for a cracked rotor system via moving feature fusion based deep learning approach

This study proposes a novel approach for predicting the remaining useful life (RUL) of a cracked rotor system by using a moving fusion gated recurrent unit (MFGRU) model. Firstly, 12 multi-domain features were extracted from the raw collected vibration signals, which were then fused and distributed adaptively using bidirectional exponential moving average (EMA) and multi-head attention (MHA). Then a bidirectional gated recurrent unit network combined with moving feature fusion method was proposed to capture the long-term dependence relationship in the monitoring data of the cracked rotor for RUL prediction. Finally, the model’s performance was validated through accelerated life experiments, with the measured values of root mean square error (RMSE) and mean absolute error (MAE) below 3.17 and 2.60, respectively. This study offers a dynamic RUL prediction method with certain significance and valuable reference for designing deep learning models.

Wear state identification of ball screw meta action unit based on parameter optimization VMD and improved Bilstm

In this paper, a novel neural network based on parameter optimization Variational Mode Decomposition (VMD) and improved bidirectional long short-term memory (BiLSTM) is proposed. Wear state recognition method of ball screw element action unit component based on Bilstm. Firstly, Tent chaotic map and adaptive sine cosine Algorithm were used to improve the improved Northern Goshawk Optimisation Algorithm (INGO) to verify the superiority of INGO algorithm and determine the optimal parameter combination of VMD. Secondly, INGO-VMD was used to decompose the collected vibration signals and calculate the correlation of IMF components, and the multi-feature information matrix that could characterize the wear state change of the lead screw was constructed after retaining the IMF components with large correlation. Finally, the divided feature information matrix and labels were input into the Bilstm network model of Bayesian optimization (BO) for training, and the Softmax classifier was used to classify and identify the wear state category.

Fault diagnosis of a wave energy converter gearbox based on an Adam optimized CNN-LSTM algorithm

The complex structure and harsh operating environment of wave energy converters can result in various faults in transmission components. Environmental noise in practical operating situations may obscure the effective information in collected vibration signals, significantly increasing the difficulty of fault diagnosis. This paper presents a fault diagnosis model for the gearbox of the point absorber wave energy converter. The model integrates a convolutional neural network with long short-term memory to realize efficient extraction of local features from signals and enhance the performance in time-series analysis. Moreover, the model incorporates the Adaptive Moment Estimation algorithm to address the situations where gradients within tensors exhibit unstable changes in the model. A rigid-flexible coupled dynamics simulation model is developed to simulate vibration signals used to train and verify the fault diagnosis model. Experimental tests of the proposed model on a vibration dataset acquired from real vibration experiments demonstrate its efficacy in diagnosing various types of faults under interference of operating conditions. Comparative studies with other models demonstrate the superiority of the proposed model in terms of fault feature extraction, learning convergence efficiency, and diagnostic accuracy, indicating that the proposed model can achieve faster and more accurate fault diagnosis of wave energy converter gearboxes.

Efficient nanoscale characterization of wafer surfaces using intelligent sampling and Bayesian optimization

The geometrical attributes of a wafer, such as thickness, uniformity, and local curvature, serve as eminent determinants of its quality. Therefore, measurements that are both rapid and accurate are paramount as multi-stage fabrication processes hinge on them to ensure high product reliability, proper process control, and maximum efficiency. The manual wafer profiling measurement approach in use today is, unfortunately, a time-consuming process. This creates a need for fast and prompt analysis of wafer attributes. This research thus proposes an iterative sampling method that boasts reducing the number of samplings down to a minimum without the need to discard a sufficient level of accuracy for the purpose of reliable attribute estimation. Subsequently, we suggest a series of methods to improve the monitoring and evaluation of the surface topography, all within the context of ultra-precision/nanoscale manufacturing processes, such as ultra-precision machining (UPM) and chemical–mechanical planarization (CMP). For the purpose of both real-time and exception-based detection of anomalies in the UPM process, we constructed a process-machine interaction (PMI) model with Bayesian learning, which amalgamates the received data across heterogeneous sensors such as force, vibration, and acoustic emission in situ. Similarly, the CMP process is quantitatively characterized by a multi-scaled multilayer nonlinear decomposition model with predictive capabilities. This model helps understand the physio-mechanical phenomena underlying both CMP and the UPM processes and describe and predict the nonlinear interactions, as evidenced by the experimentally collected vibration signal patterns.

Application of On-Rotor Sensing in Planetary Gearbox Fault Diagnosis

Planetary gearbox is a crucial mechanical transmission component, widely used in various mechanical equipment and industrial fields. Its failure will not only cause significant economic losses, but also may bring threats to the life and health of workers. Therefore, the condition monitoring and fault diagnosis of planetary gearbox is of great significance. In this paper, a novel on-rotor sensing (ORS) technique based on a three-axis MEMS accelerometer is employed to collect vibration signals from planetary gearboxes, in which the accelerometer is mounted on the shaft near to the sun gear and rotates together with the shaft. The two orthogonal outputs of the collected vibration signals is used to construct an analytic signal and then demodulated to get the envelope spectrum, which is then used diagnose the fault in the planetary gearbox. Results show that the proposed method can effectively diagnose the sun gear crack fault, while conventional piezoelectric sensors placed on planetary gearbox casing are less capable of detecting such faults, demonstrating the superiority of ORS technology in the field of rotating machinery fault diagnosis.

A novel fault diagnosis method for permanent magnet synchronous motor based on VS-Inception

The new energy vehicle industry has an increasing demand for safety, stability, and efficiency in the operation of essential equipment, particularly in the intelligent means of fault diagnosis of motor equipment, which is rising annually. For the past several years, there has been an increasing demand for accuracy and precision in troubleshooting fault diagnosis of permanent magnet synchronous motors. This paper presents a variable-scale Inception (VS-Inception) permanent magnet motor malfunction diagnostic method on the basis of R parameters. Firstly, the Time Series Generative Adversarial Network is utilized to expand the dataset samples of the collected vibration signals, resulting in many virtual signals with motor fault characteristics. Then, the variable parameter variable scale structure of the VS-Inception model is employed to carry out the fault diagnosis of permanent magnet synchronous motors on the expanded dataset. Finally, comparison experiments of VS-Inception with the existing state-of-the-art methods and with the original method are conducted to confirm the superiority even further and stability of the VS-Inception method in the field of permanent magnet synchronous motor malfunction diagnostic.

A state estimation method based on CNN-LSTM for ball screw

Ball screw is widely used in the engineering field, and accurate estimation of their state is crucial for the reliability of system operation. However, existing methods often overlook the time series characteristics and spatial correlation of vibration signals, unable to provide complete degradation information and divide the degradation process, resulting in limited prediction accuracy. Therefore, a state estimation method for ball screw based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Neural Networks (LSTM) is proposed. An experiment of ball screw transmission equipment was conducted to collect vibration signals throughout the entire life cycle and verify the proposed method. Firstly, the frequency domain amplitude signal of the transformed ball screw is normalized to eliminate scale differences, which serves as the input for CNN feature extraction. Then, these deep features are input into the LSTM network to capture the fault evolution patterns that reveal the degradation of ball screw performance, and achieve accurate estimation of ball screw state. The final prediction accuracy was 97.87%, verifying the effectiveness of the proposed method.

Efficient and Secure Communication in Vanets Using Block Chain

This project describes the design and implementation of a non-contact vibration picker for capturing data from spinning machinery in order to detect bearing faults early. The Hilbert transform is used to denoise the collected vibration signals, and the dataset is then analyzed by Principal Component Analysis (PCA) and Sequential Floating Forward Selection (SFFS) for dimensionality reduction and feature selection, respectively. The most essential attributes are then used to identify and categorize various bearing issues using Support Vector Machines (SVM) and Artificial Neural Network (ANN) algorithms. The entire methodology provides an effective and proactive way for bearing health monitoring and maintenance, emphasizing fast defect identification and leading to significant savings in time, effort, and equipment maintenance costs.

A Multi-scale Attention-Based Transfer Model for Cross-bearing Fault Diagnosis

Bearings are key components of mechanical equipment, and fault diagnosis is a necessary and important measure to ensure bearing safety. Driven by industrial big data and deep learning (DL), intelligent fault diagnosis (IFD) has made great progress in recent years. However, most of the existing methods mainly focus on the fault diagnosis of individual bearings, and the feature extraction and fault classification rely on traditional networks and expert experience, which cannot meet the diagnostic requirements of cross-bearing conditions. To fill this research gap, this paper proposes a multi-scale attention-based transfer model (MSATM). First, the collected vibration signals are converted into time–frequency maps as samples, and the proposed MSATM employs multi-scale residual learning and attention mechanism to adaptively extract sensitive fault features, and recognizes faults of new bearings by deep transfer learning using the trained MSATM. A large number of experimental results based on a bearing benchmark validate the effectiveness and superiority of the proposed method and provide a promising tool for cross-bearing fault diagnosis.

Proactive Fault Detection in Rotating Machinery using Machine Learning- A Survey

This study presents a survey of approaches for proactive fault detection in rotating machinery, with a focus on the early identification of bearing faults to enhance equipment reliability and operational efficiency. Traditional methods, relying on physical sensors and manual inspections, often lack the ability to provide timely insights into emerging faults. In contrast, the surveyed approaches integrate non-contact vibration sensors with advanced machine learning techniques, revolutionizing fault detection capabilities. The study presents a brief overview of the methods used in classification of the rotating machinery defects using the machine learning and recommends a combination of machine learning methods at different stages to overcome the challenges of the traditional methods. The collected vibration signals undergo noise reduction via the Hilbert transform, followed by dimensionality reduction and feature selection using Independent Component Analysis (ICA) and Genetic Algorithms (GA), respectively. The selected features are then employed for fault detection and categorization using Random Forest (RF) and Deep Belief Networks (DBN). The Future work will involve the implementation and evaluation of these approaches in real-world industrial settings to validate their effectiveness and reliability.

Sparrow search mechanism-based effective feature mining algorithm for the broken wire signal detection of prestressed concrete cylinder pipe

To enhance the capability of distributed optical fiber (DOF) monitoring system on real-time broken wire signal detection of prestressed concrete cylinder pipe (PCCP), this research proposes a novel effective parameter mining algorithm by utilizing wrapper theory and sparrow search algorithm (SSA). First, the improvement approach of SSA is investigated by applying the adaptive adjustment strategy, the Gaussian mutation perturbation, and the Tent chaotic perturbation. The subset search method is constructed based on the improved SSA (ISSA). Second, the similarity degrees of vibration signals are characterized by the Euclidean distance and the average linkage distance. On this basis, the subset evaluation criterion is established according to the classification accuracy of hierarchical clustering (HC). Finally, the prototype experiment is conducted in which the phase-sensitive optical time-domain reflectometry (Φ-OTDR) monitoring system is employed to collect vibration signals. Then, the effectiveness and the performance of the developed methodology are validated. The following results are drawn. (1) Benchmark function tests indicate that the convergence performance, the optimization ability, and the operation stability of ISSA are superior to those of SSA and the improved particle swarm optimization (IPSO). (2) The original set that has 122 feature parameters is established, and ISSA-HC, IPSO-HC, and SSA-HC extract 24, 31, and 55 effective parameters, respectively. (3) The recognition accuracies of the 3 parameter subsets are 95.83%, 84.17%, and 80.83%, respectively. The parameter subset mined by ISSA-HC has the best pattern recognition ability. This research provides technical support for the broken wire monitoring of in-service PCCP engineering. Meanwhile, it offers a foundation for further developing suitable pattern recognition algorithms to identify PCCP broken wire signals.

Multi-layer convolutional dictionary learning network for signal denoising and its application to explainable rolling bearing fault diagnosis

As a vital mechanical sub-component, the health monitoring of rolling bearings is important. Vibration signal analysis is a commonly used approach for fault diagnosis of bearings. Nevertheless, the collected vibration signals cannot avoid interference from noises which has a negative influence on fault diagnosis. Thus, denoising needs to be utilized as an essential step of vibration signal processing. Traditional denoising methods need expert knowledge to select hyperparameters. And data-driven methods based on deep learning lack interpretability and a clear justification for the design of architecture in a “black-box” deep neural network. An approach to systematically design neural networks is by unrolling algorithms, such as learned iterative soft-thresholding (LISTA). In this paper, the multi-layer convolutional LISTA (ML-CLISTA) algorithm is derived by embedding a designed multi-layer sparse coder to the convolutional extension of LISTA. Then the multi-layer convolutional dictionary learning (ML-CDL) network for mechanical vibration signal denoising is proposed by unrolling ML-CLISTA. By combining ML-CDL network with a classifier, the proposed denoising method is applied to the explainable rolling bearing fault diagnosis. The experiments on two bearing datasets show the superiority of the ML-CDL network over other typical denoising methods.