Joint Fault Research Articles

Distributed Energy-Based Estimation Over Harvesting-Constrained Sensor Networks.

This article investigates the distributed joint state and fault estimation issue for a class of nonlinear time-varying systems over sensor networks constrained by energy harvesting. It is assumed that data transmission between sensors requires energy consumption, and each sensor can harvest energy from the external environment. A Poisson process models the energy harvested by each sensor, and the sensor's transmission decision depends on its current energy level. One can obtain the sensor transmission probability through a recursive calculation of the probability distribution of the energy level. Under such energy harvesting constraints, the proposed estimator only uses local and neighbor data to simultaneously estimate the system state and the fault, thereby establishing a distributed estimation framework. Moreover, the estimation error covariance is determined to possess an upper bound, which is minimized by devising energy-based filtering parameters. The convergence performance of the proposed estimator is analyzed. Finally, a practical example is presented to verify the usefulness of the main results.

A Joint State and Fault Estimation Scheme for State-Saturated System with Energy Harvesting Sensors.

In this article, the issue of joint state and fault estimation is ironed out for delayed state-saturated systems subject to energy harvesting sensors. Under the effect of energy harvesting, the sensors can harvest energy from the external environment and consume an amount of energy when transmitting measurements to the estimator. The occurrence probability of measurement loss is computed at each instant according to the probability distribution of the energy harvesting mechanism. The main objective of the addressed problem is to construct a joint state and fault estimator where the estimation error covariance is ensured in some certain sense and the estimator gain is determined to accommodate energy harvesting sensors, state saturation, as well as time delays. By virtue of a set of matrix difference equations, the derived upper bound is minimized by parameterizing the estimator gain. In addition, the performance evaluation of the designed joint estimator is conducted by analyzing the boundedness of the estimation error in the mean-squared sense. Finally, two experimental examples are employed to illustrate the feasibility of the proposed estimation scheme.

Determining Smart Phone Sensing and K-Means Clustering for Accurate and Timely Railway Track Joint Fault Diagnosis

The railway track joint is an important component that connects two sections of the rail and ensures a smooth and safe operation of trains. However, the joint is also a critical point of failure that can lead to train derailments and accidents. Therefore, accurate and timely detection of joint faults is crucial for ensuring the safety and reliability of railway transportation. In this paper, we propose a novel approach for railway track joint fault diagnosis using smart phone sensing and k-means clustering. Our approach utilizes the accelerometer sensor of a smart phone to measure the vibrations and movements of a specifically developed railway shoe stick that is employed on an actual railway track for the condition monitoring of the railway tracks. More than 60000 data values are collected and are then processed and analysed using k-means clustering, a popular unsupervised machine learning technique that groups similar data points together. The K means clustering in this study forms 3 clusters as a result. The 3 clusters after being validated on the track by virtue of visual inspection are determined to be acceleration values of the healthy track, track with higher joint gap than the standardized value and super-elevated railway track joint fault(s), respectively. In addition to its high accuracy and efficiency, our approach has several advantages over traditional methods, such as low cost, easy deployment, and high scalability. Moreover, the smart phone sensing technology can be easily integrated with existing train monitoring systems, making it a useful tool for real-time joint fault diagnosis and maintenance. Overall, this study demonstrates the potential of smart phone sensing and k-means clustering for railway track joint fault diagnosis and highlights the need for further research in this field.

A Joint Fault-Tolerant and Fault Diagnosis Strategy for Multiple Actuator Faults of Full-Vehicle Active Suspension Systems

A Joint Fault-Tolerant and Fault Diagnosis Strategy for Multiple Actuator Faults of Full-Vehicle Active Suspension Systems

Distributed Joint Fault Estimation for Multi-Agent Systems via Dynamic Event-Triggered Communication

Distributed Joint Fault Estimation for Multi-Agent Systems via Dynamic Event-Triggered Communication

Data-Driven Joint Fault Diagnosis Based on RMK-ASSA and DBSKNet for Blast Furnace Iron-Making Process

Blast furnace iron-making process (BFIP) is one of the most critical procedures in the iron and steel industry where timely detection and accurate classification of faults have always been of core focus. However, the coupling effects of system’s nonlinear and nonstationary characteristics often cause process consistent underlying information to be buried, allowing accurate extraction to be a significant challenge. This also complicates the development of BFIP fault diagnosis model. Therefore, we propose a novel data-driven joint fault diagnosis strategy that employs regularized mutual kernel analytic stationary subspace analysis (RMK-ASSA) and deep broad stationary kernel network (DBSKNet) to eliminate this interference. To develop this method, we first construct an RMK-ASSA approach to address the poor modeling accuracy caused by standard analytic stationary subspace analysis (ASSA)’s inability to handle complex process nonlinearity. Global and local kernels are utilized to account for multiple nonlinearities in BFIP data. The weight of different nonlinear data is calculated by regularized principal component analysis, and the main information is imported into ASSA to obtain more robust and accurate modeling results by eliminating the interference of redundant noise. Subsequently, we design a DBSKNet-based classifier to implement the fault diagnosis task. This network further considers the nonlinearity by boosting kernel structure in depth and width while distinguishing the respective contributions of different kernels to fault diagnosis results. Finally, a double-layer loop parameter optimization algorithm is used for optimizing. Simulated cases and practical BFIP tests validate that RMK-ASSA eliminates the negative impact caused by nonstationary data and that the proposed joint fault diagnosis strategy outperforms other methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —BFIP’s nonlinear and nonstationary coupling properties pose unique challenges in eliminating distractions, constructing fault classifiers and accurately detecting process anomalies. To tackle these challenges, this paper proposes a joint fault diagnosis strategy based on RMK-ASSA and DBSKNet. RMK-ASSA effectively estimates nonlinear consistent features, while DBSKNet mines rich deep nonlinear information, accurately distinguishing variations in BFIP data under different working conditions. Experimental results demonstrate that this data-driven strategy can perform high-quality fault diagnosis, enabling field engineers to execute operations efficiently.

Manipulator joint fault localization for intelligent flexible manufacturing based on reinforcement learning and robot dynamics

This article proposes a new method to address the challenge of remote monitoring in intelligent flexible manufacturing systems. Specifically, we propose a multi working condition fault localization algorithm for robotic arms, which eliminates the need for additional sensors and is based on the classic sliding window algorithm. We use reinforcement learning technology to learn detection parameter debugging experience under different working conditions, and combine the dynamics of the robot to achieve fault detection and fault source localization in a flexible environment. Through the robot's own programmable logic controller system, the remote monitoring system can sense the operating status of each link. To evaluate the effectiveness of our proposed method, we conducted experimental equipment simulations and real-world industrial operations. The results show that under multiple operating conditions, the accuracy of fault detection reaches 86%, and the accuracy of localization reaches 81.35%. The deviation of results under different robot operating conditions is significantly lower than other algorithms. This study explores the potential and implementation approaches of reinforcement learning in intelligent manufacturing systems, with a particular focus on applications in flexible scenarios. These findings reveal the prospects of reinforcement learning technology in improving the sustainable operation of intelligent manufacturing systems.

Joint feedback control and fault diagnosis disambiguation

This paper proposes a model-based diagnosis approach to detect and isolate intermittent faults in complex systems that operate under feedback control. The feedback control attempts to compensate for model uncertainties and deviations from nominal behavior, but these uncertainties are crucial for accurate fault diagnosis. We focus on faults that are observable only in a particular region of the state space, which is rarely reached in nominal behavior. To address this, we present an approach that considers both control requirements and diagnosis uncertainty in an optimization problem similar to model-predictive control. We compute perturbations on control signals that forces the system to reach states where faults are detectable. We apply our approach to a quadrotor system under motion feedback control, demonstrating the effectiveness of our method. Our approach has the potential to improve the resilience of complex systems by quickly detecting and recovering from disruptive events.

Joint state and fault estimation for nonlinear systems with missing measurements and random component faults under Round-Robin Protocol

This work studies the fault estimation problem for a class of nonlinear systems subjected to missing measurements and random component faults under the Round-Robin protocol. Considering that the communication bandwidth is not infinite in the actual projects, the Round-Robin protocol is used for data scheduling, in which sensor nodes cyclically transmit data to the controller/filter. Meanwhile, component faults are also considered which usually caused by actuator faults. It should be emphasized that the introduction of RR protocol will make the system output equation more complex, the MMs and component faults will generate more unknown parameters, which will bring new challenges during the design of the filter. In respect of the problems above, a recursive joint state and fault estimation (JSFE) algorithm is proposed to solve the difficulties caused by missing measurements, component faults, and nonlinearity. The performance of the designed JSFE filter is analyzed by using the boundedness theorem and the reasonable parameter condition is derived for this filter. Finally, an engineering example is given to verify the effectiveness and feasibility of the proposed joint state and fault estimation scheme. This study provides a new theoretical basis and reference for the JSFE of nonlinear systems with mixed faults under RR protocol.

Unbiased state and fault estimation for discrete-time complex networks with time delays

In this paper, the joint state and fault estimation problem for a class of discrete-time complex networks with time delays is investigated. The information on the dynamics of the fault is not required with an appropriate structure of the proposed fault estimator. Unbiased state and fault estimation can be obtained simultaneously at each node only with locally available information, thereby avoiding the requirement of a centre node which collects information from every node. Considering the effects of time delays and the interconnections between different nodes, an upper bound of the estimation error covariance is calculated, and the desired filter parameters are obtained in a recursive manner such that the trace of the bound is minimised. The existence conditions of the developed filter are provided explicitly as well. Finally, a simulation example is employed to demonstrate the state estimation and fault diagnosis performances of the given strategy.

Evolution of grounding failure‐insulation failure of 10 kV cable joints: Prerequisites of an explosion in enclosed cable trench

AbstractAn explosion accident in an enclosed cable trench caused by the discharge of 10 kV three‐phase cable joints is discussed. Combined with the disassembly analysis, the fault recording data analysis, and the validation of experiment, the evolution of the cable joint fault is deduced and discussed. The results show that the trigger of the cable joint fault is the creepage discharge at the cross‐linked polyethylene–silicon rubber insulation interface. This results in the partial breakdown and the grounding failure of three‐phase cable joints. Under the long‐term floating potential and current thermal effect, the insulations are gradually ablated and decomposed into a large amount of combustible gas. Finally, the accumulated combustible gas is ignited by the arc caused by a three‐phase short circuit at the moment of the reclosing operation. The analytical method and conclusions proposed in this paper can provide suggestions and guidance to prevent analogous distribution network cable joint fault accidents.

Research on mechanical fault diagnosis based on MADS evidence fusion theory

In machine intelligence fault diagnostic and health status decision-making systems, rich, complex, and fuzzy feature information cannot facilitate fault decision-making merely on a single data source. This requires utilizing the heterogeneity of information gathered from multiple sources to diminish the system’s uncertainty and improve the accuracy of decision-making. In this work, a novel neural network-based multi-source fusion classification model is proposed to diagnose the pump mechanical faults. The multi-head attention Dempster–Shafer (D–S) evidence fusion (MADS) system extends the model’s ability to focus on rich features. Furthermore, the uncertain values throwing mechanism can effectively eliminate samples from uncertain categories and increase the model’s ability to distinguish diagnostic results with low confidence. Compared with a single sensor, our multi-sensor joint decision based on seven sensors considerably improved the fault diagnostic accuracy of MADS system, which has increased by at least 12.34%. Experimental validation demonstrates that utilizing the improved combination rules provided for multi-source evidence fusion fault diagnosis can significantly improve the efficacy of conventional D–S fusion and reduce the probability of misjudgment; combining the multi-head attention mechanism can dramatically increase the precision of model fault diagnosis. The proposed method has the potential to substantially accelerate research in the field of multi-source sensor joint fault diagnosis.

Fault detection of automobile suspension system using decision tree algorithms: A machine learning approach

The study aims to detect multiple faults that are exhibited by suspension system components during prolonged usage. Faults such as strut worn out, strut external damage, strut mount fault, lower arm ball joint fault, lower arm bush worn out and tie rod ball joint fault were considered in this study. A novel approach is proposed in the present study that involves vibration signals and machine learning techniques to identify various suspension system faults. Vibration signals were acquired for different fault conditions (as mentioned above) at three different load conditions by a specially fabricated experimental setup. Statistical features were extracted from the acquired vibration signals from which the most significant features were selected using J48 decision tree algorithm. The selected features were provided as input to the tree-based family of algorithms to determine the best in class classification algorithm for suspension fault diagnosis. The results obtained enumerate that the random forest classifier produces the best classification accuracy for all the load conditions (no load, half load, and full load) with values of 95.88%, 94.88%, and 92.01%, respectively. Finally, the performance of the proposed classification model is compared with other state-of-the-art machine learning classifiers.

An integrated framework via key-spectrum entropy and statistical properties for bearing dynamic health monitoring and performance degradation assessment

Dynamic health monitoring (DHM) and performance degradation assessment (PDA) is critical for mechanical bearings throughout their long in-service life. For this issue, it is currently rare to find a framework with interpretable and automatic approaches developed from pure signal processing techniques and statistical theories. Therefore, an integrated framework via key-spectrum entropy and statistical properties for bearing DHM and PDA is developed in this paper, which integrates the proposed key spectrum, key-spectrum entropy, joint statistical alarm and fault identification strategy, health phase segmentation strategy, and three-dimensional (3D) key spectrums. First, a Kurtosis-Energy metric is defined to extract the key spectrum, which is reconstructed by two wavelet-decomposed sub-bands where the interference components are suppressed. A new health index (HI) of key-spectrum entropy is then defined to quantify the bearing degradation process. Second, a joint statistical alarm and fault identification strategy via updated HIs and key spectrum is proposed to form a DHM methodology for implementing bearing dynamic fault detection and recognition. Third, a health phase segmentation strategy and 3D key spectrums are developed to form a PDA methodology for implementing bearing health phase assessment and degradation pattern analysis. Comprehensive evaluations and comparisons on eighteen sets of bearing degradation vibration signals demonstrate the validity of the proposed framework, as well as its great practical application prospects.

Deep residual neural-network-based robot joint fault diagnosis method

A data driven method-based robot joint fault diagnosis method using deep residual neural network (DRNN) is proposed, where Resnet-based fault diagnosis method is introduced. The proposed method mainly deals with kinds of fault types, such as gain error, offset error and malfunction for both sensors and actuators, respectively. First, a deep residual network fault diagnosis model is derived by stacking small convolution cores and increasing the core size. meanwhile, the gaussian white noise is injected into the fault data set to verify the noise immunity for the proposed deep residual network. Furthermore, a simulation is conducted, where different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), convolutional neural network (CNN), long-term memory network (LTMN) and deep residual neural network (DRNN) are compared, and the simulation results show the accuracy of fault diagnosis for robot system using DRNN is higher, meanwhile, DRNN needs less model training time. Visualization analysis proved the feasibility and effectiveness of the proposed method for robot joint sensor and actuator fault diagnosis using DRNN method.

Study on a 110 kV combined prefabricated joint fault caused by premature failure of grounding grid

The grounding grid on the outside of the epoxy insulator breaks under the long-term oxidative corrosion of moisture, fails prematurely, and may eventually develop into a breakdown failure. However, the impact of this process has not yet been discovered.In this paper, a 110 kV combined prefabricated cable joint breakdown fault is discussed. Through the inspection and comparative analysis of the faulty joint, it is speculated that the premature failure of the grounding grid causes the breakdown of the epoxy insulator. The fault process can be divided into the grounding failure stage and the insulation damage stage. In this paper, the corresponding two-dimensional electric field simulation calculation model is established, the fault development process is restored, and the possible effects of different stages is analyzed.The results show that due to the corrosive effect of moisture, when the grounding grid is broken, a floating potential will be generated on the surface of the epoxy insulator. With the expansion of the grounding failure range, the local field strength on the surface of the epoxy insulator is distorted, which will cause partial discharge. When the failure range is 220 mm, the electric field distortion of the joint reaches the maximum value of 23.3MV/m, which will destroy the insulation strength of the epoxy insulator and cause insulation breakdown, eventually leading to an accident.The research results point out the importance of the continuity of the grounding grid, and the improvement measure is proposed to reduce the occurrence of such accidents. The analysis method for cable joint failure case can provide a reference for dealing with similar accidents.

Fault diagnosis of robot joint based on BP neural network

AbstractAiming at the problem of low accuracy of robot joint fault diagnosis, a fault diagnosis method of robot joint based on BP neural network is designed. In this paper, the UR10 robot is taken as the research object, and the end pose data of the robot are collected in real time. By injecting different joint errors and changing the sampling frequency, the joint fault database is collected and established, and the BP neural network is used for training to obtain the robot neural network fault diagnosis model. The fault diagnosis model can output the joint fault of the input end pose data. And we analyzed the influence of different joint angle errors and different training sets on the accuracy of joint fault diagnosis of the robot. The results show that when the sampling frequency is 250 Hz, the simulation result of joint fault diagnosis accuracy with the fault degree of 0.5° is 99.17%, and the experimental result is 97.87%. Compared with traditional data-driven methods, it has higher accuracy and diagnostic efficiency, and compared with existing machine learning methods, it also achieves a high accuracy while reducing the network complexity. The effectiveness of the BP neural network robot joint fault diagnosis method is verified by experiments.

Joint state and fault estimation for nonlinear complex networks with mixed time-delays and uncertain inner coupling: non-fragile recursive method

In this paper, the non-fragile joint state and fault estimation problem is investigated for a class of nonlinear time-varying complex networks (NTVCNs) with uncertain inner coupling and mixed time-delays. Compared with the constant inner coupling strength in the existing literature, the inner coupling strength is permitted to vary within certain intervals. A new non-fragile model is adopted to describe the parameter perturbations of the estimator gain matrix which is described by zero-mean multiplicative noises. The attention of this paper is focussed on the design of a locally optimal estimation method, which can estimate both the state and the fault at the same time. Then, by reasonably designing the estimator gain matrix, the minimized upper bound of the state estimation error covariance matrix (SEECM) can be obtained. In addition, the boundedness analysis is taken into account, and a sufficient condition is provided to ensure the boundedness of the upper bound of the SEECM by using the mathematical induction. Lastly, a simulation example is provided to testify the feasibility of the joint state and fault estimation scheme.

Identification and assessment method of cable joint health status based on traveling wave reflection.

Due to the difficulty in accurately detecting the health status of cable joints, a new method of identifying and assessing the cable joints' health status based on traveling wave reflection is proposed. By transmitting nanosecond pulse signals at the head end of the cable, the health status characteristics of the cable joints can be obtained, and the noise reduction of the measurement signals is performed by the wavelet function to identify the reflected wave at the joint and the end of the cable. The equivalent circuits and simulation models of contact resistance and leakage resistance are established, and the contact resistance and leakage resistance of the cable joints are calculated according to the amplitude and phase of the reflected signals at the joint and the end of the cable. By comparing with the resistance when the joint is in healthy operation, any measured parameter exceeding the corresponding standard is considered a joint fault. Simulations and test results show that the method can accurately identify the reflected signals of the cable joints in different operating states, and with changes in joint health, clear regular features can be extracted from the reflected signals, and the quality and health of the joints can be determined. The traveling wave method can be used to detect cable joint faults and provide an early warning of defects, which provides a new idea for online monitoring and early warning of cable joints in both theory and engineering practice.

Minimum-Variance State and Fault Estimation for Multirate Systems With Dynamical Bias

This brief is concerned with the joint state and fault estimation problem for a class of multi-rate systems with dynamical bias. To reflect real practice, the multi-rate sampling is considered which allows the sensor sampling rate and the state update rate to be different. The sensor is subject to the sensor fault that changes according to a dynamic equation. Instead of applying the traditional lifting technique, we introduce a time-varying delay into the measurement equation so as to transform the multi-rate systems into single-rate ones. The aim of this brief is to develop a joint state and fault estimation algorithm with minimized estimation error covariance. The recursion of the estimation error covariance is first derived, and appropriate estimator gains are then characterized that minimizes the estimation error covariance. A simulation example on the DC servo system is given to confirm the usefulness of the developed recursive state and fault estimation algorithm.