Fault Diagnosis System Research Articles

Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems

The world’s energy demand is on the rise, leading to an increased focus on renewable energy options due to global warming and rising emissions from fossil fuels. To effectively monitor and maintain these renewable energy systems connected to electrical grids, efficient methods are needed. Early detection of PV faults is vital for enhancing the efficiency, reliability, and safety of PV systems. Thermal imaging emerges as an efficient and effective technique for inspection. On the other hand, evidence indicates that monitoring inverters within a solar energy farm reduces maintenance expenses and boosts production. Optimizing the efficiency of solar energy farms necessitates comprehensive analytics and data on every inverter, encompassing voltage, current, temperature, and power. In this study, our objective was to perform two distinct fault analyses utilizing image processing techniques with thermal images and machine learning techniques using inverter and other physical data. The results show that hotspot and bypass failures on the panels can be detected successfully using these methods.

A Novel Method for Predicting the Behavior of a Sucker Rod Pumping Unit Based on the Polished Rod Velocity

Fault dynamometer cards are the basis of the diagnosis technique for sucker rod pumping systems. Predicting fault cards with a pumping condition model is an economical and effective method. The usual model is described by a mixed function of the pump displacement and pump load, and it is difficult to use in the prediction method based on the analytical solution of the sucker rod string wave equation. In this paper, a normal pumping condition model described by a function of polished rod velocity is proposed. For the analytical solution of the sucker rod wave equation, an iterative prediction algorithm with pumping condition models is proposed, its convergence is analyzed, and then it is validated by classical finite difference method simulated cards and measured surface dynamometer cards. The results show that the proposed algorithm is accurate. The algorithm has a maximum relative error of 0.10% for the classical method simulated card area and 1.45% for the measured card area. The research of this paper provides an effective scheme for the design, prediction, and fault diagnosis of a sucker rod pumping system with an analytical solution.

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

Abstract Hydraulic component faults have the characteristics of nonlinear time-varying signal, strong concealment, and difficult feature extraction, etc. Timely and accurately fault diagnosis of hydraulic components is helpful to curb economic losses and accidents, so researches have carried out a lot of research on hydraulic components. Information fusion technology can combine multi-source data from multiple dimensions to mine fault data characteristics, which effectively improves the accuracy and reliability of fault diagnosis results. However, there is currently a lack of a comprehensive and systematic review in this domain. Therefore, in this paper, the hydraulic components information fusion fault diagnosis technologies are summarized and analyzed, including the main process information fusion fault diagnosis and the research status of information fusion fault diagnosis of hydraulic system. The methods and techniques involved in the fusion process, data source and fusion method of fault diagnosis of hydraulic components information fusion are described and summarized. The problems of information fusion in fault diagnosis of hydraulic components are analyzed, the solutions are discussed, and the research ideas of improving information fusion fault diagnosis are put forward. Finally, digital twin technology is introduced, and the advantages and research status of intelligent fault diagnosis based on digital twin are summarized. On this basis, the intelligent fault diagnosis of hydraulic components based on information fusion is summarized, and the challenges and future research ideas of applying information fusion and digital twin to intelligent fault diagnosis of hydraulic components are put forward and analyzed comprehensively.

A multi-output fault diagnosis framework for hydraulic system using a CNN-SVM hierarchical learning strategy

Achieving asymptotic and concurrent fault diagnosis in hydraulic system remains a challenging endeavor due to the inherent attributes of the hidden occurrence, simultaneous manifestation, coupling, and limited sample size. To address the above issues, this paper proposes a hierarchical multi-output fault detection and diagnosis framework, namely, HMDF, based on a hierarchical learning strategy to leverage an improved convolutional neural network (CNN) and support vector machine (SVM). Both a multi-channel CNN and a multi-branch CNN are employed to extract and downscale features collected by the sensors at diverse sampling frequencies first, and then, such features are subsequently subjected to classification using SVM. The hierarchical learning strategy enables the identification of different fault states, both at the component and the intra-component level. Additionally, a modified whale optimization algorithm is also utilized to optimize the classification process of SVM. Extensive experiments are conducted to test the proposed HMDF with the hydraulic system datasets. Results show that HMDF achieves a diagnostic accuracy of up to 98.9% for the dataset, surpassing traditional methods reliant on manual extraction of time–frequency features, and it also exhibits superior classification performances with a small sample size. The HMDF is expected to offer a generalized framework for the multi-output fault detection and diagnosis in hydraulic systems and other complex components.

Fault Diagnosis of Hydraulic Components Based on Multi-Sensor Information Fusion Using Improved TSO-CNN-BiLSTM.

In order to realize the accurate and reliable fault diagnosis of hydraulic systems, a diagnostic model based on improved tuna swarm optimization (ITSO), optimized convolutional neural networks (CNNs), and bi-directional long short-term memory (BiLSTM) networks is proposed. Firstly, sensor selection is implemented using the random forest algorithm to select useful signals from six kinds of physical or virtual sensors including pressure, temperature, flow rate, vibration, motor power, and motor efficiency coefficient. After that, fused features are extracted by CNN, and then, BiLSTM is applied to learn the forward and backward information contained in the data. The ITSO algorithm is adopted to adaptively optimize the learning rate, regularization coefficient, and node number to obtain the optimal CNN-BiLSTM network. Improved Chebyshev chaotic mapping and the nonlinear reduction strategy are adopted to improve population initialization and individual position updating, further promoting the optimization effect of TSO. The experimental results show that the proposed method can automatically extract fusion features and effectively utilize multi-sensor information. The diagnostic accuracies of the plunger pump, cooler, throttle valve, and accumulator are 99.07%, 99.4%, 98.81%, and 98.51%, respectively. The diagnostic results of noisy data with 0 dB, 5 dB, and 10 dB signal-to-noise ratios (SNRs) show that the ITSO-CNN-BiLSTM model has good robustness to noise interference.

Fault diagnosis for cross-building energy systems based on transfer learning and model interpretation

Timely and accurate fault diagnosis in building energy systems is crucial for improving the energy efficiency of equipment and realizing energy savings. With the widespread use of building automation systems, data-driven deep-learning fault diagnosis methods have gained popularity due to their flexibility and accuracy. However, they have certain associated challenges, such as limited extrapolation capabilities and their black-box nature. To address these issues, this study focuses on transfer learning to improve the energy efficiency of building energy system equipment, exploring the influencing factors and behaviours of transfer learning fault diagnosis performance and model interpretation results. A transfer-learning strategy is employed, utilizing feature-level SHAP and visual interpretation methods to understand the inner workings and diagnostic mechanisms of transfer-learning models. The study constructs five transfer-learning strategies for two typical building energy systems. The results demonstrate that the proposed fine-tuning approach achieves the highest accuracy, of 94.32 %, surpassing non-transferred and shallow approaches. The interpreted methods reflect the joint diagnosis of refrigerant leakage faults by compressor discharge temperature and condenser inlet and outlet water temperature in the target chiller. The findings from both the transfer mechanism and interpretation of the deep-learning model provide valuable guidance for the development of practical deep transfer-learning models and corresponding interpretation methods. This research contributes to improving fault diagnosis accuracy and understanding the diagnostic mechanisms in building energy systems, ultimately leading to enhanced energy efficiency and energy savings.

Application of SSA-KNN Algorithm in Fault Analysis of Power Metering Automation System

In view of the great pressure of operation and maintenance of power energy data collection terminal after putting into operation, an intelligent fault diagnosis system for power equipment terminal based on computer was developed. This paper proposes an Agent based on autonomy, promotility, cooperation and interactivity. It also provides a flexible interactive communication interface and load balancing optimization method. It can meet a variety of data transmission specifications and a large number of data service transmission requirements. The SA-KNN algorithm is used to analyze the power quality. Methods The operation data of maintenance electricity were collected. Learning and cluster analysis are carried out by using relevant data, and the calculation model of automatic check device failure is established. At the same time, the outlier points in the collected data are used to accurately locate the fault points of the power grid. This method can realize the comprehensive fault diagnosis of the power grid by using the interaction between the power grid and each component.

Advanced digital twin-enabled fault diagnosis framework for unmanned vehicle systems

The control system of unmanned vehicles must demonstrate strong capability to promptly diagnose and address system faults. Such a capability can improve transportation efficiency, ensure the smooth execution of production tasks, and to a certain extent, mitigate the risk of human casualties. To ensure the upkeep of unmanned vehicles and address the diagnostic requirements of control systems, this study integrates traditional wheeled vehicle control systems with digital twin (DT) technology to establish a framework for control system fault diagnosis and maintenance, with the primary objective of fulfilling the fault diagnosis task. By this framework, a method for detecting faults in unmanned vehicle control systems based on DT technology has been developed. This method involves the design of a data-driven model using multiple sensors and the application of a DT-improved particle filter fault diagnosis algorithm, utilizing a multi-domain model approach. A case study of the proposed method and simulation results are presented to illustrate its feasibility.

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

The energy loss and substantial costs associated with faults in photovoltaic (PV) systems impose significant limitations on their efficiency and reliability. Addressing current issues in PV fault diagnosis such as the lack of typical fault data, imbalanced data distribution, and poor diagnostic performance, this paper proposes an intelligent fault diagnosis method for PV systems, deep digital twins (DDT) with information gain stacking sparse autoencoders (IGSSAEs). Initially, the method designs a novel DDT modeling framework tailored to actual PV system specifications. This framework utilizes a mechanism simulation model to generate typical data under various states. Simultaneously, a deep data model is constructed to learn the distribution characteristics of the mechanism model and complete data diversification, achieving the fusion and complementation of data from both models. Subsequently, a diagnostic network using IGSSAE is introduced. This network utilizes information gain ratio to assess feature classification contributions, enabling automatic feature selection. Based on the input features, a stacked sparse autoencoder fault classification network is designed, incorporating multi-level feature compression to enhance the model’s stability and diagnostic accuracy. Finally, a case study is conducted using a 250 kW grid-connected PV system, thoroughly validating the method’s effectiveness with a diagnostic accuracy of 98.4%.

Fault diagnosis of helicopter tail-drive system using a multi-grained hierarchical message graph convolutional networks

ABSTRACT The fault diagnosis of the tail-drive of helicopter is a crucial task for helicopter system operation and maintenance. Recently, graph convolution network (GCN) has been the focus in fault diagnosis for its powerful representational ability in relationship mining. However, with the difficulty of obtaining node and edge information in the high-order domain, the stable performance of the long-range message-passing process of the deep GCN is unknown limits the application of GCN in fault diagnosis. To address these issues, a multi-grained hierarchical message graph convolutional network (MHGCN) is proposed to diagnose faults of helicopter tail-drive system. First, time-frequency characteristics of the original vibration signals are extracted to construct the graph nodes. The original graph nodes are aggregated by Louvain community detection, which can effectively learn the multi-grained features. Then, the hierarchical graph is introduced to learn the features of high-order neighbourhoods. Finally, a particular message-passing method is used to encode long-range information spanning the graph structure and realise accurate classification. Experiments on a test rig of helicopter tail-drive system are performed to verify the efficacy of the proposed method.

Fault diagnosis in high-speed computing systems using big data analytics integrated evolutionary computing on the Internet of Everything platform

The significance of high-speed computing systems is paramount for diverse applications. However, their intricate structure and rigorous operational prerequisites render them susceptible to malfunctions. The conventional fault diagnosis models need help managing the copious data produced by these systems, resulting in diagnostic procedures that are both time-consuming and ineffective. The research proposed an Internet of Everything (IoE) platform and big data analytics (BDA) based Fault Diagnosis System (IBFDS) in conjunction with evolutionary computing techniques. The system integrates a hybrid intelligent algorithm that amalgamates the advantages of evolutionary computing and machine learning to enhance the precision and effectiveness of fault diagnosis. The utilization of big data analysis methodologies facilitates the processing and examination of vast quantities of system data, thereby enabling the comprehensive detection and identification of faults. Furthermore, cloud services based on the IoE allow data storage, administration, and instantaneous cooperation among various parties involved.
 Furthermore, a detection technique utilizing a Reduced Boltzmann Machine (RBoM) improves the precision of fault diagnosis. The efficacy of the proposed IBFDS was demonstrated through experimental evaluation using a high-speed computing system dataset. The results obtained were impressive, with a fault detection rate (94.11%), fault identification accuracy (93.76%), fault localization accuracy (93.71%), false positive rate (1.54%), and false negative rate (0.98%). The findings of this study make a valuable contribution to the advancement of fault diagnosis systems that are resilient and effective for high-speed computing systems. This facilitates proactive maintenance, enhances system reliability, and optimizes system performance.

Research on Power Equipment Fault Diagnosis Based on Improved SVM Algorithm

Recently, Data collected from safety relays and circuit breakers has been used in fault diagnostics to identify power system components that are either failing or have tripped. A tiny percentage of heating zones may cause problems when identifying faults, although the chance of aberrant heat production in power equipment is modest. The power transformer is one of the most crucial components of any electrical system. Discovering any concealed defects in power equipment is essential to ensuring a continuous supply of electrical power for customers. The challenging characteristic of such fault diagnosis in power equipment is the greater potential for harm and a great deal of time invested in testing and training. Hence, in this research, Smart Support Vector Machine enabled Artificial Neural Network (SVM-ANN) technologies have been improved for fault diagnosis in power equipment for industry. The Genetic Algorithm (GA) process the power equipment for industrial data processing and monitoring in that fault diagnosis. The classification of strategies used and their relationships to power equipment processes to identify significant trends and research problems related to intelligent fault diagnosis systems for industry. The experimental analysis of SVM-ANN outperforms fault diagnosis using the power equipment industry in terms of performance, accuracy, prediction ratio, efficiency, and error rate.

Network-based System of Mechanical Vibration Fault Diagnosis

Nowadays researchers are investing in electrical machines fault diagnosis area. The users and manufacturers are strong for containing diagnostic features for reliability and scalability improvement. The regular monitoring enables machine faults early detection and hence helpful for automation by providing process control. The fault detection performance and machine-learning algorithms classification are highly dependent on features involved. The aim of this paper is to solve the network mechanical vibration problem and for that a research on mechanical vibration fault diagnosis is proposed. The first is to use the vibration signal receiving device to record the vibration signal of the target device. In the process of receiving the signal, the measurement point is related to the accuracy of the received signal, so it is necessary to prepare the measurement point. Secondly, the principle of fault detection based on vibration detection is introduced. The main purpose of this method is to identify the fault characteristics, simulate the fault with MATLAB, and obtain the error time-frequency diagram behavior. The feature vector dimension obtained by the idling confirmation example is the same as that of the rotor, which is 14, including 8 relative wavelet packet intensity entropy feature indexes and higher values, minimum value, peak-to-peak value, mean value, mean square error and variance. Finally, the deficiencies of the detected vibration faults are identified and similar improvements are proposed. Improvements only reduce signal vibration, disrupting feature isolation and identifying patterns.The observed percentage accuracy for classification of faults through proposed approach is 98.2%.

Electric Energy Metering Error Evaluation Method Based on Deep Learning

The measuring accuracy of the electric energy meter, voltage transformer and current transformer shows a dynamic state under the influence of its factors and external factors. The error of the voltage transformer and current transformer cannot be measured by traditional method. This paper establishes a multidimensional error analysis and fault diagnosis system for power metering based on Hadoop architecture and Spark memory calculation. The platform extracted the error signal from the measurement data and calculated the characteristic value of the error signal. Then, dependent cloud and dynamic time rules are used to estimate the transformer's and voltage transformer's continuity. Then, a half-step membership degree cloud generation algorithm is constructed to overcome the error bias randomness and fuzzy characteristics under the influence factors. Finally, the system uses the dynamic correction method to estimate the similarity of error timing and quantitative factors to realize the error calculation of the current transformer and voltage transformer. The power metering error processing system was built with the support of Hadoop and Spark. The timing increment is introduced in the process of data collection. Dependent cloud and dynamic time-repair methods can improve the accuracy of diagnosing errors in electric energy metering. The parallel optimization of big data platforms by belonging to the cloud and dynamic time-warping algorithm is verified.

Centrifugal Pump Fault Detection with Convolutional Neural Network Transfer Learning.

The centrifugal pump is the workhorse of many industrial and domestic applications, such as water supply, wastewater treatment and heating. While modern pumps are reliable, their unexpected failures may jeopardise safety or lead to significant financial losses. Consequently, there is a strong demand for early fault diagnosis, detection and predictive monitoring systems. Most prior work on machine learning-based centrifugal pump fault detection is based on either synthetic data, simulations or data from test rigs in controlled laboratory conditions. In this research, we attempted to detect centrifugal pump faults using data collected from real operational pumps deployed in various places in collaboration with a specialist pump engineering company. The detection was done by the binary classification of visual features of DQ/Concordia patterns with residual networks. Besides using a real dataset, this study employed transfer learning from the image detection domain to systematically solve a real-life problem in the engineering domain. By feeding DQ image data into a popular and high-performance residual network (e.g., ResNet-34), the proposed approach achieved up to 85.51% classification accuracy.

A data-driven hybrid sensor fault detection/diagnosis method with flight test data

Fault diagnosis of multi-source signal systems is critical for the safe and reliable operation of modern industrial systems, and accurate fault diagnosis of systems based on multi-source signals remains challenging. This study proposes a data-driven hybrid fault detection/diagnosis method to identify sensor faults in complex systems through interactions between multiple sensors. Dynamic mode decomposition with control is used to obtain the approximate model of the investigated system from multi-source signals, extract the underlying physical mechanisms, combined with the Kalman filter observer to generate the residual between the observed data and the predicted data. Then the residual (moving innovation covariance matrix V k ) is input into the k-nearest neighbor classification algorithm for fault detection and diagnosis. The effectiveness of the proposed method was evaluated using the flight test braking system dataset. The results showed that the accuracy of the proposed method in fault detection and diagnosis (with accuracies of 100% and 100%, respectively) was significantly improved than that of using raw signal data (76.6% and 6.38%) or raw signal data and Vk (80.85% and 42.55%). The analysis of different parameters including fault severity, algorithm hyperparameter k , and sensor type showed that the proposed method has high robustness, generalization ability, and practicality.

Non-invasive real-time diagnosis of PMSM faults implemented in motor control software for mission critical applications

The machine’s health should be continuously monitored, or a test should be applied on a regular basis to predict failure before fatal damage is incurred. Unexpected failures, particularly in mission-critical applications, can cause irreversible damage to the system, or even human life. This paper introduces a novel non-intrusive, real-time, online Condition Monitoring (CM), and Fault Diagnosis (FD) system for Permanent Magnet Synchronous Machines (PMSMs). Only the motor drive’s built-in sensors, such as current and position sensors, are used to detect three types of faults: inter-turn short circuit, partial demagnetization, and static eccentricity. It encompasses the implementation of algorithms within a motor drive system and the creation of failure mode models. The proposed solution adopts a hardware-free approach, utilizing current/voltage signature analysis for cost-effectiveness. It requires a small memory and short execution time, allowing it to be implemented on a simple motor controller with limited memory and calculation power. The drive system is intended for mission critical applications, therefore, computation load, code size, memory allocation, run-time optimization, etc. are the key focuses for real-time operation. It offers immediate insights into motor’s health without interrupting the drive operation. Additionally, it ensures rapid processing with modest computational requirements, making it adaptable for implementation on any PMSM controller. The non-intrusive nature of this diagnostic approach has the potential to enhance safety in systems reliant on PMSM drives. The proposed method has a high detection accuracy of 98%, is computationally efficient and can detect and classify the fault accurately. Simulation and experimental results demonstrate the efficiency of the FD algorithm for online identification and classification of machine faults. Theoretical hypotheses are proven based on experimental data.

A novel dust collection system fault diagnosis based on the noise emission of solenoid pulse valve

The solenoid pulse valve (SPV) is a key component of dust collector. The frequent switch of SPV accelerates its damage, subsequently decrease operation efficiency of dust collector. The traditional manual inspection methods lack reliability and efficiency, while vibration-based condition monitoring is costly and sophisticated in acceleration sensor distribution and signal processing. This paper proposes a fault diagnosis approach based on SPV noise. The average coherence coefficient of SPV noise and vibration signals is above 0.8 and close to 1, which demonstrates that there is a close relationship between SPV noise and its structural vibration. Furthermore, the validation experiments indicate that the SPV fault can be clearly distinguished by its noise signal, confirms the effectiveness of proposed approach. By utilizing this noise monitoring method, the number of sensors can be reduced by about 95 %, the work intensity of the staff is greatly reduced, and production efficiency and safety are guaranteed.

Convolutional Deep Belief Network Based Expert System for Automated Fault Diagnosis in Hydro Electrical Power Systems

The paper developed an approach for fault diagnosis in Hydro-Electrical Power Systems (HEPS). Using a Renewable Energy System (RES), HEPS has performed a significant part in contributing to addressing the evolving energy demands of the present. Several electro-mechanical elements that collectively comprise the Hydro-Electric (HE) system are susceptible to corrosion from routine usage and unplanned occurrences. Administration and servicing systems that are successful in implementing and achieving these goals are those that regularly track and predict failures. Detect models applied in the past included those that were primarily reactive or reliant on human involvement to identify and analyse abnormalities. The significant multiple variables intricacies that impact successful fault detection are disregarded by these frameworks. The research presented here proposes a Convolutional Deep Belief Network (CDBN) driven Deep Learning (DL) model for successful fault and failure detection in such power systems that address these problems. Applying sample data collected from two Chinese power plants, the proposed framework has been assessed compared to other practical DL algorithms. Different metrics have been employed to determine the effectiveness of the simulations, namely Accuracy, Precision, Recall, and F1-score. These outcomes indicated that the CDBN is capable of predicting unexpected failures. Graphic representations demonstrating control used to measure turbine blade load, vibration level, and generator heat for assessing the replicas.

Fault Detection and Diagnosis in Electric Vehicle Systems using IoT and Machine Learning: A Support Vector Machine Approach

This research examines blame discovery and determination in electric vehicle (EV) frameworks utilizing Internet of Things (IoT) information and machine learning calculations, centring on a Support Vector Machine (SVM) approach. The study points to improving the unwavering quality and security of EV operations by precisely recognizing and diagnosing flaws in real time. The test comes about illustrates the adequacy of the SVM-based approach, with an exactness of 95%, accuracy of 94%, review of 96%, and F1-score of 95%. Comparative investigation with elective calculations such as k-Nearest Neighbors, Decision Tree, and Random Forest underscores the predominant execution of SVM in blame discovery and determination. The SVM calculation shows negligible misclassifications over distinctive blame classes, highlighting its strength and viability. This inquiry contributes to the headway of blame location strategies in EV frameworks and gives profitable bits of knowledge into the commonsense usage of machine learning strategies for improving framework unwavering quality. Moving forward, the discoveries clear the way for assist investigations in optimizing blame location systems and expanding their appropriateness to other spaces such as mechanical mechanization and renewable vitality frameworks.