Fault Simulation Experiments Research Articles

Fault diagnosis of drone motors driven by current signal data with few samples

Multi rotor unmanned aerial vehicles (UAVs) are extensively utilized across various domains, and the motor constitutes a pivotal element in the UAV power system. The majority of UAV failures and crashes stem from motor malfunctions, underscoring the imperative need for comprehensive research on fault diagnosis in UAV motors to ensure the stable and reliable execution of flight tasks. This study focuses on quadrotor UAVs as the research subject and devises targeted fault simulation experiments based on the structural features and operational characteristics of the DC brushless motor used in quadrotor UAVs, specifically examining the stator, rotor, and bearings. To address challenges related to the UAV’s own loads, limited space for redundant parts, and the high cost and difficulty associated with installing sensors for traditional fault diagnostic signals such as vibration and temperature, this study opts to use current signals as a substitute. This approach resolves the issue of challenging data collection for UAVs and investigates a current signal based fault diagnosis method for UAV motors. Lastly, in response to the limited training samples available for fault data due to the UAV’s highly sensitive characteristics regarding the health status of its components and flight stability, traditional machine learning and deep learning methods encounter difficulties in identifying representative features with a small number of training samples, leading to the risk of overfitting and reduced model accuracy in fault diagnosis. To overcome this challenge, we propose a hybrid neural network fault diagnosis model that incorporates a width learning system and a convolutional neural network (CNN). The width learning system eliminates temporal characteristics from the original current signal, capturing more comprehensive and representative sample features in the width feature space. Subsequently, the CNN is employed for feature extraction and classification tasks. In empirical small sample fault diagnosis experiments using current signal data for UAV motors, our proposed model outperforms other models used for comparison.

Enhancing equipment safeguarding in IIoT: A self-supervised fault diagnosis paradigm based on asymmetric graph autoencoder

Thanks to the sufficient monitoring data provided by Industrial Internet of Things (IIoT), intelligent fault diagnosis technology has demonstrated remarkable performance in safeguarding equipment. However, the effectiveness of existing methods heavily relies on manually labeled data. Unfortunately, data collected from equipment often lacks labels, leading to a scarcity of fault data. Furthermore, an additional significant challenge is the feature domain shift resulting from speed variation. To address this, we propose a self-supervised paradigm based on an asymmetric graph autoencoder for fault diagnosis under domain shift, aiming to mine valuable health information from unlabeled data. Unlike Euclidean-based methods, the proposed method transforms time series samples into graphs and extracts domain invariant features through information interaction between nodes. To efficiently mine unlabeled data and enhance generalization, the self-supervised learning paradigm utilizes an asymmetric graph autoencoder architecture. This architecture includes an encoder that learns self-supervised representations from unlabeled samples and a lightweight decoder that predicts the original input. Specifically, we mask a portion of input samples and predict the original input from learned self-supervised representations. In downstream task, the pre-trained encoder is fine-tuned using limited labeled data for specific fault diagnosis task. The proposed method is evaluated on three mechanical fault simulation experiments, and the results demonstrate the its superiority and potential.

A Multiple Attention Convolutional Neural Networks for Diesel Engine Fault Diagnosis.

Fault diagnosis can improve the safety and reliability of diesel engines. An end-to-end method based on a multi-attention convolutional neural network (MACNN) is proposed for accurate and efficient diesel engine fault diagnosis. By optimizing the arrangement and kernel size of the channel and spatial attention modules, the feature extraction capability is improved, and an improved convolutional block attention module (ICBAM) is obtained. Vibration signal features are acquired using a feature extraction model alternating between the convolutional neural network (CNN) and ICBAM. The feature map is recombined to reconstruct the sequence order information. Next, the self-attention mechanism (SAM) is applied to learn the recombined sequence features directly. A Swish activation function is introduced to solve "Dead ReLU" and improve the accuracy. A dynamic learning rate curve is designed to improve the convergence ability of the model. The diesel engine fault simulation experiment is carried out to simulate three kinds of fault types (abnormal valve clearance, abnormal rail pressure, and insufficient fuel supply), and each kind of fault varies in different degrees. The comparison results show that the accuracy of MACNN on the eight-class fault dataset at different speeds is more than 97%. The testing time of the MACNN is much less than the machine running time (for one work cycle). Therefore, the proposed end-to-end fault diagnosis method has a good application prospect.

A fault diagnosis method for nuclear power plants rotating machinery based on deep learning under imbalanced samples

Rotating machinery is a critical equipment widely used in nuclear power plants (NPPs). Effective fault diagnosis technology can provide reliable operation and maintenance support for rotating machinery. Data-driven intelligent fault diagnosis technology has attracted much attention in recent years. However, in practical situations, the available fault data is limited, and the imbalanced samples will make the intelligent fault diagnosis model prone to problems such as poor generalization performance and low diagnostic accuracy. Therefore, a fault diagnosis method based on deep learning is proposed to alleviate the impact of imbalanced samples on fault diagnosis of rotating machinery. First, the adaptive synthetic sampling (ADASYN) approach is used to synthesize the multi-channel vibration data to expand the unbalanced samples. Subsequently, ensemble empirical mode decomposition (EEMD) and continuous wavelet transform (CWT) are used to convert the vibration data into time–frequency images to highlight the fault features of the samples. Then, the deep residual neural network is constructed to extract the features of mixed samples and implement fault diagnosis. Fault simulation experiments are carried out based on motors and bearings, and various imbalance degree datasets are formed to provide data support for verifying the effectiveness of the proposed method. The proposed method can achieve good diagnostic performance under various degrees of imbalanced samples. In addition, the comparison results with other methods show that the proposed method has the best comprehensive performance, demonstrating the potential application value in the fault diagnosis of NPPs rotating machinery.

Dimensionality reduction of rotor fault dataset based on joint embedding of multi-class graphs

Traditional dimensionality reduction techniques usually rely on a single or a limited number of similar graphs for graph embedding, which limits their ability to extract more information about the internal structure of the data. To address this problem, this study proposes a rotor fault dataset dimensionality reduction algorithm based on multi-class graph joint embedding (MCGJE). The algorithm first overcomes the defect that the traditional feature space cannot take both local and global information into account by constructing local and global median feature line graphs; secondly, based on the graph embedding framework, the algorithm also constructs a hypergraph structure for inscribing complex multivariate relationships between high-dimensional data in the feature space, which in turn enables it to contain more fault information. Finally, we conducted two different rotor fault simulation experiments. The results show that the MCGJE-based algorithm has robust dimensionality reduction capability and can significantly improve the accuracy of fault identification.

Self-driven continual learning for class-added motor fault diagnosis based on unseen fault detector and propensity distillation

Continual learning is a high-potential technique that enables intelligent motor fault diagnosis models to extend new diagnosable fault classes without costly training from scratch. However, existing continual learning methods have the following limitations. (1) They manually detect new faults, which is labor-intensive, untimely, and more importantly, may lead to mistaken diagnosis results. (2) They adopt the traditional knowledge distillation to align the absolute responses of old and new models, which alleviates catastrophic forgetting but restricts flexible learning from incremental datasets. To overcome the above limitations, this paper proposes a novel self-driven continual learning framework for class-added motor fault diagnosis, which can spontaneously detect unseen faults and perform more flexible continual learning from incremental datasets. For the automatic detection of unseen faults, after collecting online samples, adversarial training with exemplars of each seen class is conducted to measure the class separability. The truth fault classes that are unseen for diagnosis models can be clearly distinguished from all seen classes, and correspondingly missed diagnosis or misdiagnosing can be avoided effectively and incremental samples with new fault types can be collected quickly. For the flexible continual learning strategy, a more flexible knowledge distillation is proposed to preserve the prediction propensity rather than the absolute response. This strategy not only keeps the recognition performance of old classes but also loosens unnecessary constraints and increases the diagnosis model plasticity to learn new knowledge from incremental datasets, thus improving the accuracy of motor fault diagnosis during continual learning. The effectiveness of the proposed method is verified by conducting fault simulation experiments of three-phase motors and its superiority is also demonstrated by comparing it with some state-of-the-art diagnosis methods.

Mechanical fault diagnosis of gas-insulated switchgear based on saliency feature of auditory brainstem response under noise background

The mechanical fault of gas-insulated switchgear (GIS) seriously threatens the security of the power grid. Recently, acoustic-based fault diagnosis methods, which have the advantage of non-contact measurement, have been applied to the GIS mechanical fault diagnosis, but vulnerable to the interference of the background noise. To improve the capacity of the acoustic-based GIS fault diagnosis under noise background, by simulating the sound feature extraction ability and anti-noise ability of human auditory system, a novel GIS mechanical fault diagnosis method based on saliency feature of auditory brainstem response (SFABR) is proposed. First, an auditory saliency model, which considers both the auditory periphery and the auditory nerve center was constructed by combining the deep auditory model and the saliency model. After processing GIS emitted acoustic signal, the auditory brainstem response (ABR) was obtained, and the saliency features of the ABR were extracted to obtain the SFABR. Then, the characteristic frequency distribution of the auditory saliency model was adjusted to make it more suitable for the spectral characteristics of the GIS sound signal. Finally, the SFABR was mapped to a two-dimensional CNN to train a model for GIS mechanical fault diagnosis. This method simulates the process of auditory response extraction and the selection effect of auditory attention on sound elements. The 110 kV three-phase GIS fault simulation experiment shows that for GIS mechanical faults, the diagnosis method based on SFABR can obtain 96.1% fault identification accuracy. In different noise environments, compared with the traditional acoustic-based fault diagnosis methods, this method has stronger anti-noise performance, and can more effectively realize the identification of GIS mechanical failure types. In future research, the method can be further extended to fault diagnosis of more types of power equipment.

Feature-level consistency regularized Semi-supervised scheme with data augmentation for intelligent fault diagnosis under small samples

Intelligent fault diagnosis based on machine learning has yielded a wealth of research results. However, fault diagnosis under small samples is still a challenging problem due to the difficulty of collecting fault data of machines in engineering scenarios. To address this problem, this paper proposes a feature-level consistency regularized semi-supervised scheme with data augmentation for fault diagnosis of machines. In the proposed method, generative adversarial networks generate unlabeled data to augment the limited training set. The fault identification network in the proposed method is trained by the augmented data and a small amount of real data collected from machines in a semi-supervised learning manner. We construct a distance evaluation metric based on the Earth Mover's Distance (EMD) and further design a novel feature consistency regularization module, which helps the fault identification network learn robust fault features by minimizing the EMD between generated data features and real data features. To verify the effectiveness of the proposed model, two mechanical fault simulation experiments were carried out. The experimental results show that the proposed method achieves high fault diagnosis accuracy using only a small amount of training data samples.

Multi-sensor signals multi-scale fusion method for fault detection of high-speed and high-power diesel engine under variable operating conditions

Detecting faults in high-speed and high-power diesel engines under complex variable operating conditions is highly challenging. Online vibration monitoring systems have been used in such diesel engines in key fields, in which vibration sensors are installed on each cylinder to enable comprehensive monitoring. In this paper, a fault detection method for diesel engines under variable operating conditions is proposed based on multi-sensor signal multi-scale fusion. Firstly, a preprocessing framework is established for the raw vibration signals collected from each cylinder to eliminate random interference and system noise. Then, the resulting signals are phase-aligned based on the engine firing sequence and analyzed using a signal correlation algorithm to produce a multi-sensor multi-scale similarity matrix (MSMSSM). Finally, a multi-branch residual convolutional neural network (MBRCNN) model is constructed with the MSMSSM as the input to detect abnormal health states of the diesel engine. Fault simulation experiments are conducted on a 12-cylinder V-type high-speed and high-power diesel engine test rig. The comparative test results indicate that the proposed MSMSSM-MBRCNN method shows both the highest accuracy of 95.28% and the lowest standard deviation of 3.57% compared to other typical methods. The multi-sensor signals multi-scale fusion method proposed in this paper fully utilizes the key information that remains basically consistent in the synchronous acquisition signals of multiple sensors under different operating conditions. This can effectively reduce the interference of operating condition changes and improve the accuracy and robustness of fault detection.

Network lightweight method based on knowledge distillation is applied to RV reducer fault diagnosis

As a core component of industrial robots, the RV reducer directly affects the normal operation of the robot, so it is of great significance to monitor its status and diagnose faults. In the field of fault diagnosis, intelligent diagnosis methods based on deep learning have shown great advantages in accuracy and efficiency. However, as the network depth and scale increase, the exponentially growing model computation and parameter amounts require higher hardware requirements for computers, making it difficult to deploy on embedded platforms with limited computing resources. This makes it difficult for deep learning-based fault diagnosis methods to be applied in practical industrial settings that emphasize real-time performance, portability, and accuracy. This paper proposes a network lightweight method based on knowledge distillation (KD). Using the two-dimensional time–frequency map of vibration signals as the model input, the improved MobileNet-V3 network is used as the teacher network, and the simplified convolutional neural network is used as the student network (SN). The method of KD is applied to condense the knowledge and transfer it to the SN. The proposed method is validated using an RV reducer fault simulation experiment platform, and the results show that the proposed method reduces computation and parameter amounts by about 170 times at an accuracy rate of 94.37%, and run time is shortened by nearly one-third, and a generalization verification was conducted using the rotating mechanical fault simulation experiment platform. The models were also deployed on embedded devices to verify that the method proposed in this paper effectively reduces the deep learning network model’s demand for hardware resources of the operating environment. This provides an effective reference for deploying and implementing deep learning-based fault diagnosis on embedded systems with lower hardware configurations.

Non-Intrusive Arc Fault Detection and Localization Method Based on the Mann–Kendall Test and Current Decomposition

In recent years, electrical fires caused by arc faults have been increasing, seriously affecting the safety of people’s lives and property. Considering the complex arc fault characteristics of actual low-voltage users, the non-intrusive arc fault detection and localization method is studied. First, the characteristics of arc current waveforms are analyzed, and event detection based on the Mann–Kendall Test is performed for the difference between the current waveforms of two adjacent cycles, rather than using the current waveforms directly. Then, the current waveforms of the two segments are calculated via subtraction to obtain the current waveform of the electric appliances causing the event. A current feature parameter database of the normal and arc currents is constructed via harmonic analysis, and a multi-appliance current decomposition model considering the sparse operation characteristics of appliances is established; thus, the arc localization problem is transformed into an optimization problem. Finally, a genetic algorithm is used to optimize the differential current decomposition results, and then, locate the arc fault. A household arc fault simulation experiment is carried out for the common electric appliances of actual low-voltage users. The experimental results show that the proposed non-intrusive arc fault detection and localization method is effective.

Interpretable mechanism mining enhanced deep learning for fault diagnosis of heating, ventilation and air conditioning systems

Various faults in the heating, ventilation, and air conditioning (HVAC) systems may lead to high energy consumption and maintenance costs. Reliable fault detection and diagnosis (FDD) are essential for HVAC systems to minimize safety and efficiency risks. Most of current FDD studies established by deep learning have high accuracy, but lack of interpretability and generalization. This paper proposes an interpretable mechanism mining enhanced deep learning method for FDD model transfer among different HVAC systems. Firstly, three kinds of fault simulation experiments are carried out on two types of chillers to provide data for the diagnosis process. Secondly, a one-dimensional convolutional neural network (1D-CNN) is trained for the FDD task of the source chiller. Then score-weighted class activation mapping method (Score-CAM) is used to explain why the 1D-CNN can diagnose faults well and find six key variables consistent with expert experience. Finally, a general FDD model is obtained by retraining the original model with the extracted key variables, which is verified on another different type of chiller with two cycles. The testing results indicate that the retrained transfer model has a good diagnostic effect for the target chiller, the accuracy for the first and second cycles of the chiller are 80.27% and 73.73%, respectively. The proposed framework of FDD model transfer has significant performance advantages and practical application potential.

A fault diagnosis of nuclear power plant rotating machinery based on multi-sensor and deep residual neural network

Rotating machinery is a key component of nuclear power plants (NPPs). The integrity of rotating machine is related to the safety and economy of the entire NPPs. In order to achieve better and more robust diagnostic performance, this work proposes an intelligent fault diagnosis method based on multi-sensor and deep residual neural network. This method gives full play to the value of multi-sensor information, and utilizes the powerful learning ability of deep learning model to realize the identification of rotating machinery fault types. The effectiveness of the method is evaluated by using the motor dataset and the bearing dataset from fault simulation experiment bench. In addition, the anti-noise ability of the method is tested and compared with other methods. The results show that the proposed method has higher diagnosis accuracy and stronger robustness, demonstrating the potential application value in rotating machinery of NPPs.

Fault identification and simulation experiment of helicopter transmission gear based on stress distribution

Fault identification and simulation experiment of helicopter transmission gear based on stress distribution

Modeling of instantaneous angular speed variation of large clearance cylindrical roller bearing with local defects in rolling element

The instantaneous angular speed (IAS) has been gradually applied to bearing fault identification. The IAS of the fault bearing have periodic disturbance by bearing dynamic modeling and fault simulation experiment in some studies. However, few of them has involved the local characteristics of IAS periodic disturbance, especially the large clearance bearing, which may lead to misdiagnosis. In this paper, a bearing rolling element fault dynamic model is proposed by considering the contact stiffness between the rolling elements and races based on the Hertzian contact theory. Moreover, A fault with 0.25 mm width and 0.2 mm depth was seeded on the roller of the bearing using the laser to verify the validity of simulation results. The results show that the periodic impulse is observed in the IAS, which is mainly due to the sudden change of torque when the defect of the rolling element contacts the raceway, especially in the load area.

Prior Knowledge-Augmented Self-Supervised Feature Learning for Few-Shot Intelligent Fault Diagnosis of Machines

Data-driven intelligent diagnosis models expect to mine the health information of machines from massive monitoring data. However, the size of faulty monitoring data collected in engineering scenarios is limited, which leads to few-shot fault diagnosis as a valuable research point. Fortunately, it is possible to reduce the required amount of training data by integrating prior diagnosis knowledge into diagnosis models. Inspired by this, we present a prior knowledge-augmented self-supervised feature learning framework for few-shot fault diagnosis. In the framework, 24 signal feature indicators are built to form prior features set based on existing diagnosis knowledge. Besides, a convolutional autoencoder is used to mine the general features, which are considered to potentially contain fault information that prior features do not possess. We design a self-supervised learning scheme for training the diagnosis model, which enables the model to learn both prior and general features served as proxy labels. As a result, the model is expected to mine richer features from limited monitoring data. The effectiveness of the proposed framework is verified using two mechanical fault simulation experiments. From the angle of prior diagnosis knowledge, the proposed framework provides a new perspective to the problem of few-shot intelligent diagnosis of machines.

A novel multi-channel weighted convolutional neural network for fault identification of high-voltage circuit breakers

To solve the problem that single-channel vibration signal input cannot fully express fault feature information and early diagnosis of high-voltage circuit breaker (HVCB) faults, a multi-channel weighted convolutional neural network (MCW-CNN) is proposed and applied to HVCB vibration signal characteristic learning and fault diagnosis. Firstly, wavelet transform is used to extract the time-frequency features of the vibration signal. The vibration signal is converted into multi-channel image input, so as to give full play to the excellent performance of CNN in image feature extraction. Secondly, according to the difference in image frequency and bandwidth of each channel, the dynamic receptive field is used in the convolution layer to extract image features. Aiming at the difference in the intensity of shock features carried by each channel image, a multi-channel fusion method based on kurtosis weighting is proposed to enhance the channel fault features with strong shock features. Finally, the effectiveness of the proposed method is verified by fault diagnosis test of HVCB. Through the fault simulation experiment, the results verify that the MCW-CNN can effectively extract the fault features of the vibration signal, and the recognition accuracy is significantly higher than that of the traditional method.

Series Arc Fault Diagnosis Based on Variational Mode Decomposition and Random Forest

In order to improve the accuracy of series arc fault detection and prevent fire accidents caused by series arc fault, a series arc fault simulation experiment circuit was built to obtain the low-frequency and high-frequency current waveform of series arc fault under different loads. The kurtosis, waveform factor, crest factor, pulse factor, and margin factor of low-frequency current waveform are extracted in the time domain. In the frequency domain, a method based on variational mode decomposition and energy entropy is proposed to extract the characteristic quantity of series arc faults. It was found that the energy entropy of the intrinsic mode function component with the largest variance contribution ratio will increase when a series of arc faults occur, and it was used as a characteristic quantity. Characteristic vectors were constructed based on time–frequency characteristic quantities, and the characteristic vector was trained based on the random forest algorithm to obtain the diagnosis model and analyze the series arc fault diagnosis. The analysis showed that the diagnostic accuracy of the model trained by the proposed method was above 97%, and the fault recognition effect was remarkable, which provides an important reference for the improvement of the series arc fault detection technology.

Selecting Sensitive Features Using CDET for Bearing Fault Diagnosis

The rolling bearing fault detection technology solution can be used to maintain the machinery in advance and avoid downtime and safety accidents. However, the current studies primarily focus on utilizing complex algorithms to improve the accuracy of fault diagnosis, while neglecting the influence of extracted features on diagnosis results, resulting in redundancy and conflict among features. To address such an issue, a feature selection method based on compensation distance evaluation technique (CDET) is proposed to optimize the feature set extracted from acoustic emission (AE) signals. Firstly, multiple failure data of the bearing are modeled by the fault simulation experiment platform. Then, the proposed feature selection method is used to evaluate the sensitive features. Finally, hidden markov modelsupport vector machine (HMM-SVM) series model is used to predict works. The experimental results show that the proposed method selects 7 sensitive features from the feature set composed of 13 features and achieve a high accuracy (99.3%).

Real-Time Monitoring of High Voltage Switch Based on CAD

In the process of online real-time monitoring of intelligent digital filter high-voltage switchgear, the mechanical state diagnosis of high-voltage circuit breaker is based on fully understanding the mechanical characteristics of each component of circuit breaker. In this paper, K-means clustering algorithm is applied to the mechanical state detection of digital filter high-voltage switchgear. The mechanical state of the digital filter high-voltage switchgear is monitored in real time by using 126 kV GIS. Through fault simulation experiment and mechanical stability experiment, the corresponding changes of characteristics of each mechanical component of circuit breaker in signal waveform and corresponding data changes are found. The experimental results show that this method has good running speed and stability and is more suitable for the real-time monitoring of intelligent digital filter high-voltage switchgear.