Fault Diagnosis Capability Research Articles

A fault diagnosis method for bearings and gears in rotating machinery based on data fusion and transfer learning

Abstract Rotating machinery is a crucial component of industrial equipment, and the fault diagnosis of bearings and gears, as vital elements of rotating machinery, is essential since they often fail under harsh working conditions, leading to significant property losses and serious personal safety problems. However, fault data for gears and bearings are often sparse in actual condition, and it is a challenge to ensure the reliability and stability of fault diagnosis results by extracting the features of a single data. To solve the above problems, this paper proposes a fault diagnosis method that combines Transfer Learning and data fusion techniques. Firstly, in this method, two kinds of fault signals are transformed into Gramian Angular Difference Fields and Recurrence Plot. Next, a U-shaped feature fusion dual discriminator generative adversarial network is used to fuse two-dimensional images from multiple sensor data. Its feature fusion module deeply integrates the features of the two images, thereby solving the impact of single data on the reliability and stability of fault diagnosis. Moreover, open-source datasets are used for Transfer Learning training to tackle the small sample problem. Finally, a decision-level information fusion classifier, the Dual-Branch Dempster-Shafer Classifier (DB-DSC), classifies the fused images. This classifier incorporates an improved soft threshold function and D-S evidence theory to achieve adaptive gradient changes and improve the robustness and accuracy of classification results. The experimental results show the effectiveness and stability of the proposed method, and the generated images get high score in several metrics. The average classification accuracy of the classification network reaches 93% and 92.5% on the two datasets, Therefore, the proposed method exhibits strong fault diagnosis capabilities under the small sample conditions of bearings and gears.

Two-Stage Hyperelliptic Kalman Filter-Based Hybrid Fault Observer for Aeroengine Actuator under Multi-Source Uncertainty

An aeroengine faces multi-source uncertainty consisting of aeroengine epistemic uncertainty and the control system stochastic uncertainty during operation. This paper investigates actuator fault estimation under multi-source uncertainty to enhance the fault diagnosis capability of aero-engine control systems in complex environments. With the polynomial chaos expansion-based discrete stochastic model quantification, the optimal filter under multi-source uncertainty, the Hyperelliptic Kalman Filter, is proposed. Meanwhile, by treating actuator fault as unknown input, the Two-stage Hyperelliptic Kalman Filter (TSHeKF) is also proposed to achieve optimal fault estimation under multi-source uncertainty. However, considering that the biases of the model are often fixed for the individual, the TSHeKF-based fault estimation is robust and leads to inevitable conservativeness. By adding the additional estimation of the unknown deviation in state function caused by probabilistic system parameters, the hybrid fault observer (HFO) is proposed based on the TSHeKF and realizes conservativeness-reduced estimation for actuator fault under multi-source uncertainty. Numerical simulations show the effectiveness and optimality of the proposed HFO in state estimation, output prediction, and fault estimation for both single and multi-fault modes, when considering multi-source uncertainty. Furthermore, Monte Carlo experiments have demonstrated that the HFO-based optimal fault estimation is less conservative and more accurate than the Two-stage Kalman Filter and TSHeKF, providing better safety and more reliable aeroengine operation assurance.

MSTKernel Net: a rolling bearing intelligent diagnosis framework based on short-time time–frequency convolution

Abstract Monitoring rotating machinery is a key task in modern production processes. The emergence of deep learning technology has significantly improved the performance of intelligent diagnosis systems for such machinery. However, despite the commendable performance of many existing frameworks, they lack transparency, which hinders their interpretability in fault diagnosis based on directional signals. This study addresses this challenge by delving into the fault features present in vibration signals and designing a convolutional module specifically tailored to these characteristics, modularized short time–frequency kernel (MSTKernel). This innovative framework, MSTKernel Network, employs convolutional neural networks for feature extraction, simulating the time–frequency sliding process through convolutional properties while preserving temporal features and enriching fault diagnosis information. Through experimental data testing and visualization of convolutional kernel characteristics, we evaluate the potential of this framework to significantly enhance the fault diagnosis capability of rolling bearings, demonstrating its practicality and effectiveness in real-world applications.

Transfer learning based cross-process fault diagnosis of industrial robots

In the actual industrial application of robots, the characteristics of robot malfunctions change accordingly as the working environment becomes increasingly diverse and complex. Utilizing the original fault diagnosis models in new working environments correspondingly leads to a decline in the performance and the generalization capability of the model. Moreover, the monitoring data collected in new working processes often has limited or no labels, making the diagnosis models trained with this data unable to identify faults accurately. In this paper, we propose a Domain adaptive Cross-process Fault Diagnosis method (DCFD) to leverage knowledge from existing working processes for diagnosing faults in new working processes. DCFD uses Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to measure the difference between the current working processes and the previous working processes, enhancing the fault diagnosis capability of the robotic system in cross-process scenarios. DCFD achieves an average fault classification accuracy of 98% on 12 types of migration tasks, which demonstrates the effectiveness of DCFD on cross-process fault diagnosis classification tasks in real-time industrial application scenarios.

Wavelet-powered hierarchical frequency filtering framework for autonomous vehicle sensors fault diagnosis and correction under open environments

The reliable operation of autonomous vehicles heavily depends on the functionality of numerous sensors that facilitate environmental perception and vehicle control. However, sensor malfunctions can significantly undermine the dependability and safety of autonomous vehicles. Despite the significant advancements made in sensor fault diagnosis through deep learning, the technology's susceptibility to noise and limited frequency analysis capacity renders it less effective in addressing frequency aliasing and noise interference issues. In addition, repairing incorrect sensor data holds greater significance for autonomous vehicles than merely identifying sensor failures. To this end, this paper proposes a wavelet-powered hierarchical frequency filtering framework (WavePHF) for autonomous vehicle sensors' fault diagnosis and correction. First, this study designs a multi-sensor fault identification and correction joint architecture to detect sensor faults and repair fault signals in time. Second, this study integrates the discrete wavelet transform (DWT) algorithm into the deep model to significantly enhance the model's frequency analysis capabilities and suppress frequency aliasing and noise interference. In particular, DWT is deeply integrated into the convolutional modules' parameter updating and optimization process, thereby facilitating mutual learning and promotion. Third, this study proposes an adaptive frequency weighting block for low-frequency features and an adaptive frequency discarding block for high-frequency features to achieve hierarchical frequency filtering in a coarse-to-fine manner and efficiently identify fault categories and restore pure signals. The WavePHF is evaluated on a real autonomous driving dataset. Experiments show that WavePHF has excellent autonomous vehicle sensors fault diagnosis and correction capabilities and competitive performance in different urban scenarios.

Unsupervised transfer learning for fault diagnosis across similar chemical processes

Fault diagnosis plays a crucial role in chemical processes to prevent major accidents. Recent advancements have leveraged deep learning to enhance fault diagnosis capabilities significantly. However, the success of deep learning diagnosis models primarily relies on access to the extensive labeled dataset of faults. Another limitation is the variation of ambient temperature and raw material components contributing to diverse operating conditions. These challenges lead to the current fault diagnosis techniques being unsuitable for universal application across different devices. In this paper, a novel unsupervised transfer learning method called multiple source domain adaptation network (MSDAN) is proposed for industrial fault diagnosis across similar chemical processes. Owing to the limited availability of fault data in industrial manufacturing, fault samples for model training are expanded via an efficient generative adversarial network variant. Categorical features of various source domains are precisely extracted through distinct channels by an integration model of Transformer and multiscale convolutional neural network. Multi-channel Domain adaptation based on polynomial kernel-induced maximum mean discrepancy aligns the joint distributions of multiple domains and facilitates subsequent classification tasks. The effectiveness and robustness of the proposed method are demonstrated through experiments conducted on the multimode Tennessee Eastman process and the real-world fluid catalytic cracking process across different units.

A novel fault diagnosis method for Bayesian networks fusing models and data

Data-driven methods are the mainstream methods applied in current fault diagnosis research; however, in natural mechanical systems, fault samples are difficult or even impossible to obtain, and physical model-based methods cannot easily model the faults of complex systems. To address the respective limitations of data-driven and model diagnosis methods in fault diagnosis, in this paper, a Bayesian network fault diagnosis method based on a joint model and data modelling is proposed. Conditional Gaussian Bayesian networks are used to fuse discrete and continuous information, and the discrete information in the residuals is used to compensate for the insufficient feature information problem caused by small amounts of data. To address the exponential modeling complexity growth in the case of multiple fault types cases, a single classification method is chosen for fault classification. Meanwhile, the T2 statistics distance suppression method is introduced into the Bayesian network to improve the diagnostic capabilities for new types of faults. By comparing the mainstream methods that address small sample diagnosis and the existing fusion methods, the experimental results show that in cases with small numbers of samples, the method in this paper has good fault diagnosis capabilities by effectively utilizing the model’s diagnostic results to compensate for the diagnostic errors caused by a lack of data, and it has an improved ability to recognize unknown faults.

CNN-Based Machine Tool Monitoring with STFT Image Analysis

Abstract: In the realm of machine tool operation, effective condition monitoring holds predominant importance to ensure operational reliability and safety. Leveraging deep learning methodologies, particularly convolutional neural networks (CNN), for defect identification has gained significant attention. However, inherent challenges persist, including the extraction of salient features and potential information loss while extracting the features from raw vibration signals. In response, this study proposes an intelligent approach for condition monitoring in machine tools, integrating short-time Fourier transform and convolutional neural networks (STFT-CNN). The process entails using STFT to convert one-dimensional vibration signals into time-frequency pictures, which are then fed into the STFT-CNN model to acquire and identify fault features. Furthermore, the study explores optimizing STFT parameters, such as window type, window width, and translation overlap width, across various typical window functions to improve the effectiveness of the transformation process. Within the STFT-CNN model, the utilization of stacked double convolutional layers aims to augment the model's nonlinear expression capacity, thereby facilitating robust fault diagnosis capabilities in machine tool condition monitoring applications

A feature separation simulation-assisted transfer framework for rotating machinery fault intelligent diagnosis

High-quality labeled data are crucial prerequisites for ensuring the effectiveness of fault diagnosis methods based on deep learning technology. However, in practical scenarios, providing abundant training data with accurate labels for these approaches is unfeasible owing to the constraints imposed by the operating and working conditions. To tackle this realistic challenge, we propose an innovative feature separation simulation-assisted transfer framework (FSSATF) for the fault diagnosis of rotating machinery. The primary concept of FSSATF is to leverage dynamic simulation-assisted data as a surrogate for the labeled data of actual equipment and integrate the feature separation network to explicitly extract domain-independent and fault-discriminative features from the simulated and actual domains, facilitating knowledge transfer and enhancing fault diagnosis capabilities. Specifically, we design a feature separation network consisting of two feature extractors. The special feature extractor is trained with the proposed target domain classification loss to explicitly separate the noisy features from the actual data. Moreover, our proposed domain adaptive loss function effectively narrows the distribution discrepancy between the simulated and actual data, promoting the shared feature extractor to capture domain-invariant and fault-discriminative features. Additionally, clustering learning is embedded into FSSATF to minimize the distance between samples of the same category, strengthening the model’s capabilities for feature extraction, and improving its performance in real machinery fault diagnosis. Artificially damaged and run-to-failure datasets were employed to validate the effectiveness and superiority of FSSATF. The comparative analysis results demonstrate that the fault diagnosis performance surpasses those of other advanced transfer learning fault diagnosis methods.

Enhancing Fault Diagnosis in Industrial Processes through Adversarial Task Augmented Sequential Meta-Learning

This study introduces the Adversarial Task Augmented Sequential Meta-Learning (ATASML) framework, designed to enhance fault diagnosis in industrial processes. ATASML integrates adversarial learning with sequential task learning to improve the model’s adaptability and robustness, facilitating precise fault identification under varied conditions. Key to ATASML’s approach is its novel use of adversarial examples and data-augmentation techniques, including noise injection and temporal warping, which extend the model’s exposure to diverse operational scenarios and fault manifestations. This enriched training environment significantly boosts the model’s ability to generalize from limited data, a critical advantage in industrial applications where anomaly patterns frequently vary. The framework’s performance was rigorously evaluated on two benchmark datasets: the Tennessee Eastman Process (TEP) and the Skoltech Anomaly Benchmark (SKAB), which are representative of complex industrial systems. The results indicate that ATASML outperforms conventional meta-learning models, particularly in scenarios characterized by few-shot learning requirements. Notably, ATASML demonstrated superior accuracy and F1 scores, validating its effectiveness in enhancing fault-diagnosis capabilities. Furthermore, ATASML’s strategic incorporation of task sequencing and adversarial tasks optimizes the training process, which not only refines learning outcomes but also improves computational efficiency. This study confirms the utility of the ATASML framework in significantly enhancing the accuracy and reliability of fault-diagnosis systems under diverse and challenging conditions prevalent in industrial processes.

Voltage-fault diagnosis for battery pack in electric vehicles using mutual information

The safety of battery system is compromised by the abusive operation and aging, potentially resulting in the abnormal voltage levels. Rapid detection and accurate diagnosis of voltage fault are crucial for ensuring the safety of battery packs. A battery voltage fault diagnosis method is proposed by using the mutual information in this work, which can identify faulty cells timely. Specifically, the voltage of battery pack in an electric vehicle is collected, and the mutual information of voltages between each paired-cells is calculated. The presence of faulty cells disturbs the original distribution of mutual information. Therefore, the Ryan Joiner (RJ) test is used to judge whether the normal distribution of calculated mutual information or not. The interquartile test is used to detect the outliers. The faulty cell is diagnosed by matching the cell corresponding to the outliers. The defaults resulting from an internal short circuit and mechanical vibration are analyzed and diagnosed. The method can avoid misdiagnosis caused by the mutual influence between different faults. Furthermore, the fault diagnosis capability of presented method is discussed under various states of aged cells. The experimental results verify the effectiveness, robustness, model-free, and easiest-to-implement of the presented method.

A Fault Diagnosis Method for Ultrasonic Flow Meters Based on KPCA-CLSSA-SVM

To enhance the fault diagnosis capability for ultrasonic liquid flow meters and refine the fault diagnosis accuracy of support vector machines, we employ Levy flight to augment the global search proficiency. By utilizing circle chaotic mapping to establish the starting locations of sparrows and refining the sparrow position with the highest fitness value, we propose an enhanced sparrow search algorithm termed CLSSA. Subsequently, we optimize the parameters of support vector machines using this algorithm. A support vector machine classifier based on CLSSA has been constructed. Given the intricate data collected from ultrasonic liquid flow meters for diagnostic purposes, the approach of employing KPCA to decrease data dimensionality is implemented, and a KPCA-CLSSA-SVM algorithm is proposed to achieve fault diagnosis in ultrasonic flow meters. By using UCI datasets, the findings indicate that KPCA-CLSSA-SVM achieves fault diagnosis accuracies of 94.12%, 100.00%, 97.30%, and 100% in the four flow meters, respectively. Compared with the Bayesian classifier diagnostic algorithm, this has been increased by 4.18%. And compared with support vector machine diagnostic algorithms improved by the SSA, it has increased by 2.28%.

A novel intelligent fault diagnosis method of rolling bearings based on capsule network with Fast Routing algorithm

AbstractFault diagnosis is a novel technology crucial for monitoring the proper functioning and ensuring the stability of mechanical devices and components. Nevertheless, most existing data‐driven methods for rolling bearing fault diagnosis exhibit limited diagnostic capabilities in scenarios characterized by noise interference and inadequate training data. To address this issue, this paper proposes a novel intelligent fault diagnosis method for rolling bearings based on Capsule Network with Fast Routing algorithm (FCN). Firstly, the vibration signal is transformed into a time‐frequency map through continuous wavelet transform (CWT), and the transformed time‐frequency map is input into the network model to enable the network to learn features more fully. Subsequently, this paper introduces FCNs into capsule networks, effectively mitigating the extended training times typically associated with capsule networks and reducing the demands on training equipment. Extensive experiments are conducted utilizing two distinct bearing datasets to assess the method's stability and generalization. The results of these experiments demonstrate the proposed approach's ability to maintain robust fault diagnosis capabilities, even in the presence of noise interference and limited training data. This innovative method lays the foundation for intelligent rolling bearing diagnosis and is readily adaptable to other rotating components.

A novel minority sample fault diagnosis method based on multisource data enhancement

AbstractEffective fault diagnosis has a crucial impact on the safety and cost of complex manufacturing systems. However, the complex structure of the collected multisource data and scarcity of fault samples make it difficult to accurately identify multiple fault conditions. To address this challenge, this paper proposes a novel deep‐learning model for multisource data augmentation and small sample fault diagnosis. The raw multisource data are first converted into two‐dimensional images using the Gramian Angular Field, and a generator is built to transform random noise into images through transposed convolution operations. Then, two discriminators are constructed to evaluate the authenticity of input images and the fault diagnosis ability. The Vision Transformer network is built to diagnose faults and obtain the classification error for the discriminator. Furthermore, a global optimization strategy is designed to upgrade parameters in the model. The discriminators and generator compete with each other until Nash equilibrium is achieved. A real‐world multistep forging machine is adopted to compare and validate the performance of different methods. The experimental results indicate that the proposed method has multisource data augmentation and minority sample fault diagnosis capabilities. Compared with other state‐of‐the‐art models, the proposed approach has better fault diagnosis accuracy in various scenarios.

Evaluating Contact-Less Sensing and Fault Diagnosis Characteristics in Vibrating Thin Cantilever Beams with a MetGlas® 2826MB Ribbon

The contact-less sensing and fault diagnosis characteristics induced by fixing short Metglas® 2826MB ribbons onto the surface of thin cantilever polymer beams are examined and statistically evaluated in this study. Excitation of the beam’s free end generates magnetic flux from the vibrating ribbon (fixed near the clamp side), which, via a coil suspended above the ribbon surface, is recorded as voltage with an oscilloscope. Cost-efficient design and operation are key objectives of this setup since only conventional equipment (coil, oscilloscope) is used, whereas filtering, amplification and similar circuits are absent. A statistical framework for extending past findings on the relationship between spectral changes in voltage and fault occurrence is introduced. Currently, different levels of beam excitation (within a frequency range) are shown to result in statistically different voltage spectral changes (frequency shifts). The principle is also valid for loads (faults) of different magnitudes and/or locations on the beam for a given excitation. Testing with either various beam excitation frequencies or different loads (magnitude/locations) at a given excitation demonstrates that voltage spectral changes are statistically mapped onto excitation levels or occurrences of distinct faults (loads). Thus, conventional beams may cost-efficiently acquire contact-less sensing and fault diagnosis capabilities using limited hardware/equipment.

FPGA implementation of an improved envelope detection approach for bearing fault diagnosis

This study presents an enhanced envelope detection technique implemented on a field-programmable gate array (FPGA) to diagnose bearing faults in rotating machinery. Bearing faults frequently result in machinery breakdowns, incurring substantial downtime and maintenance expenses. In our approach, we employ the Teager energy operator (TEO) to extract the vibration signal envelope. Subsequently, we subject the envelope signal to the fast Fourier transform (FFT) to generate the envelope spectrum of the vibration signal. Finally, we further refine the envelope spectrum using TEO for a second time, resulting in a pronounced fault peak that facilitates early fault detection. We evaluate the effectiveness and performance of the proposed method using two distinct types of bearing vibration signals, one being simulated and the other measured. Our findings reveal that the suggested approach outperforms traditional envelope detection methods, leading to a substantially enhanced fault diagnosis capability. For instance, when we assess the characteristic frequency ratio (FCFR) for faults in the inner and outer rings of the bearing using the proposed method, we observe that the FCFR values are significantly elevated, ranging from 160 to 330% higher compared to the analysis performed by the TEO and HT methods. Consequently, this indicates that the proposed approach has the ability to detect faults at an earlier stage than other methods. Furthermore, the FPGA-based implementation makes it suitable for critical industrial applications where rapid fault detection is essential to prevent catastrophic failures.

Intelligent fault diagnosis method of spacecraft control system based on sequence data-image mapping

<p style='text-indent:20px;'>Satellite networking, as the future development direction of aero-space, requires high-precision autonomous fault diagnosis capability for a single satellite. In this paper, aiming at the characteristics of closed-loop fault propagation and high data dimensionality of spacecraft control system, neural network algorithms are conducted to study the fault diagnosis of spacecraft high-dimensional coupled data. Based on the ground test data of a certain spacecraft, this paper converts the high-dimensional sequence data into grayscale images, and then uses Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to diagnose them respectively. The effectiveness of the methods in this paper is illustrated by comparing and validating them with three non-image-based machine learning algorithms, namely, K-NearestNeighbor, Bayesian classifier, and K-NearestNeighbor based on Principal Component Analysis.</p>

Comparative analysis of the performance of supervised learning algorithms for photovoltaic system fault diagnosis

New trends were introduced in using PhotoVoltaic (PV) energy which are mostly attributable to new laws internationally having a goal to decrease the usage of fossil fuels. The PV systems efficiency is impacted significantly by environmental factors and different faults occurrence. These faults if they were not rapidly identified and fixed may cause dangerous consequences. A lot of methods have been introduced in the literature to detect faults that may occur in a PV system such as using Current-Voltage (I-V) curve measurements, atmospheric models and statistical methods. In this paper, various machine learning techniques in particular supervised learning techniques are used for PV array failure diagnosis. The main target is the identification and categorization of several faults that may occur such as shadowing, degradation, open circuit and short circuit faults that have a great impact on PV systems performance. The results showed the technique’s high ability of fault diagnosis capability. The K-Nearest Neighbor (KNN) technique showed the best fault prediction performance. It achieves prediction accuracy of 99.2% and 99.7% Area Under Curve-Receiver Operating Curve (AUC-ROC) score. This shows its superiority in fault prediction in PV systems over other used methods Decision Tree, Naïve Bayes, and Logistic Regression.

Fault diagnosis method for Small modular reactor based on transfer learning and an improved DCNN model

Deep convolutional neural networks (DCNN) are widely applied in the realm of deep learning. This paper presents a novel approach that combines transfer learning techniques with a hybrid domain attention mechanism module to enhance and refine the DCNN architecture, consequently boosting its performance. The focus of this study is the application of the improved DCNN model to fault diagnosis within small modular reactor. We hope to improve the fault monitoring and diagnosis capabilities of small modular reactors through algorithm improvements, to enhance their safety. Comparative results demonstrate that the improved model surpasses other deep learning models in terms of convergence rate, recognition accuracy, and model size, evidencing robust generalisation capabilities.

Time-Frequency Fusion Features-Based GSWOA-KELM Model for Gear Fault Diagnosis

To improve the accuracy of gear fault diagnosis and overcome the low diagnostic accuracy of the model caused by manual parameter selection, a combined diagnostic model based on time-frequency fusion features is combined with the improved global search whale optimization algorithm (GSWOA) to optimize the fault diagnosis capability of the kernel extreme learning machine (KELM). First, the time-domain and frequency-domain features of the gear fault state are extracted separately, and feature vectors are constructed through feature fusion, which overcomes the limitations of single features. Second, the GSWOA based on three strategies is used to optimize the regularization coefficient C and kernel function parameter γ of KELM, and a GSWOA-KELM fault diagnosis model is built to avoid the problem of low fault diagnosis accuracy caused by the manual selection of KELM parameters. Finally, the public dataset from Southeast University is taken to verify the performance of the proposed model by comparing it with KELM, SSA-KELM, and WOA-KELM models. The experimental results demonstrate that the improved time-frequency fusion features-based GSWOA-KELM model shows faster convergence speed and stronger global search ability. Compared with KELM, SSA-KELM, and WOA-KELM models, the performance of the proposed model has been improved by 11.33%, 8.67%, and 1.33%, respectively.