Multiple Faults Research Articles

Torque Ripple Reduction of Model Predictive Controlled Five-Phase PMSM Drives With Open-Circuit Faults

In this article, a fault-tolerant model predictive current control method is proposed to improve the fault-tolerant ability of a five-phase permanent synchronous magnet motor drive under single and multiple open-circuit faults operations. On the principle of the optimal current control, general reference currents are derived to suppress torque ripples. To obtain the reference currents, a Fast Fourier Transformation-based current estimation model is established. The torque ripple can be reduced by modifying the reference currents in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d3-q3</i> axis transformed from the estimation model. Finally, experimental results validate the effectiveness of the developed fault-tolerant control strategy and the robustness against operating points and motor parameter mismatches.

Inferable Deep Distilled Attention Network for Diagnosing Multiple Motor Bearing Faults

Bearing, as a vital component in electric powertrains, is increasingly used globally such as in electric vehicle (EV). Their damages and faults may bring huge cost loss to the industry and even threaten personal safety. This paper proposes an inferable deep distilled attention network (IDDAN) method which is a self-attention mechanism and transfer learning-based method to diagnose and classify multiple bearing faults in various motor drive systems efficiently and accurately. Compared with convolutional networks, the self-attention-based network can better extract the global feature information and easier to benefit from large amounts of pre-training data. Its significance is to accurately classify various faults of the target machine when the labeled data of the target machine is not enough to directly train the diagnosis model. Firstly, this paper attempt to apply the self-attention-based network to build an advanced fault diagnosis model. Secondly, this paper optimizes the structure of networks through knowledge distillation (KD) technique to require a lighter and fast model. Thirdly, this paper proposes a new data augmentation strategy for 1-D vibration signals to provide large-scale pre-training samples for IDDAN. Experiments show that the self-attention mechanism-based model is more likely to benefit from large-scale data. After testing, compared with many methods and other exist similar methods, the proposed method achieves higher classification accuracy and better performance.

Mathematical Model for Fault Handling of Singular Nonlinear Time-Varying Delay Systems Based on T-S Fuzzy Model

In this paper, a mathematical model based on the T-S fuzzy model is proposed to solve the fault estimation (FE) and fault-tolerant control (FTC) problem for singular nonlinear time-varying delay (TVD) systems with sensor fault. TVD is is extremely difficult to solve and the Laplace transform is devised to build an equal system free of TVD. Additionally, the sensor fault is changed to actuator fault by the developed coordinate transformation. A fuzzy learning fault estimator is first built to estimate the detailed sensor fault information. Then, a PI FTC scheme is suggested aiming at minimizing the damage caused by the fault. Simulation results from multiple faults reveal that the FE and FTC algorithms are able to estimate the fault and guarantee the system performance properly.

Finite-Time Output Feedback Cooperative Formation Control for Marine Surface Vessels With Unknown Actuator Faults

This article studies the finite-time output-feedback cooperative formation problem of multiple marine surface vessels (MSVs) without using velocity information, where each MSV contains unknown time-varying actuator faults, ocean disturbances, and model uncertainties. Considering the performance specifications and requirements in practice, the formation errors and velocities are also required to change in a predefined compact set. Aiming at the challenge of unknown input gain caused by unknown time-varying multiplicative faults, a novel nonlinear extended state observer with an adaptive law is first constructed, which cannot only recover unmeasurable velocities from position-heading information but also simultaneously compensate for ocean disturbances, model uncertainties, and additive faults. Subsequently, combining the barrier Lyapunov function and the nonlinear tracking differentiator technique, a unified finite-time output-feedback cooperative control framework is developed, including both kinematics and kinetics, to prevent constraint deviation and avoid the computational complexity of the traditional backstepping method. Using finite-time stability theory, the designed output feedback cooperative controller can ensure that the desired cooperative performance is achieved within a finite time and tracking errors converge to a small neighborhood of the origin while ensuring that the closed-loop system is semiglobally uniformly ultimately bounded. Simulation examples are shown to demonstrate the effectiveness of the proposed control scheme.

Outage Management of Active Distribution Systems With Data Fusion From Multiple Sensors Given Sensor Failures

Complex scenarios of multiple faults, sensor failures, and the fault current from distributed generations (DGs) pose a challenge for efficient outage management in distribution systems. To solve the issue, this study proposes a new algorithm for sequential determination of outage scenarios with data fusion from multiple sensors, i.e., smart meters and remote fault indicators (RFIs). To this end, an active distribution system is modeled as an undirected graph with vertices representing load zones, protective devices and RFIs, and edges denoting their adjacency. Set operation is proposed to derive the expected RFI alarms for each load zone if faulted and smart meter outage reports for each protection device. This serves as an input to the proposed evidence-driven methodology to sequentially quantify the credibility of the potential outage scenarios supported by RFIs and smart meters. By ranking the credibility, the most credible scenario of the activated protection and fault location, either on the primary feeder or laterals, is determined for expedited power recovery. Test cases of the IEEE System and a utility circuit show that the proposed approach outperforms the state-of-the-art technologies in terms of its accuracy and computational efficiency, leading to more efficient outage management of distribution systems.

Currents Analysis of a Brushless Motor with Inverter Faults—Part I: Parameters of Entropy Functions and Open-Circuit Faults Detection

In the field of signal processing, it is interesting to explore signal irregularities. Indeed, entropy approaches are efficient to quantify the complexity of a time series; their ability to analyze and provide information related to signal complexity justifies their growing interest. Unfortunately, many entropies exist, each requiring setting parameter values, such as the data length N, the embedding dimension m, the time lag τ, the tolerance r and the scale s for the entropy calculation. Our aim is to determine a methodology to choose the suitable entropy and the suitable parameter values. Therefore, this paper focuses on the effects of their variation. For illustration purposes, a brushless motor with a three-phase inverter is investigated to discover unique faults, and then multiple permanent open-circuit faults. Starting from the brushless inverter under healthy and faulty conditions, the various possible switching faults are discussed. The occurrence of faults in an inverter leads to atypical characteristics of phase currents, which can increase the complexity in the brushless response. Thus, the performance of many entropies and multiscale entropies is discussed to evaluate the complexity of the phase currents. Herein, we introduce a mathematical model to help select the appropriate entropy functions with proper parameter values, for detecting open-circuit faults. Moreover, this mathematical model enables to pick up many usual entropies and multiscale entropies (bubble, phase, slope and conditional entropy) that can best detect faults, for up to four switches. Simulations are then carried out to select the best entropy functions able to differentiate healthy from open-circuit faulty conditions of the inverter.

Strain and Stress Accumulation in Viscoelastic Splay Fault and Subducting Oceanic Crust

AbstractThis study constructs a model to represent the strain–stress field surrounding a splay fault and demonstrates the accumulation processes of both strain and stress around the splay fault. We consider the case of multiple fault dislocations, construct the model as a function of the effective viscosity in the media, and investigate the influence of the effective viscosity on the strain and stress accumulation patterns. The results show that the strain and stress tend to accumulate in the splay fault and the subducting oceanic crust, and the rate of accumulation varies with the effective viscosity. The accumulation and relaxation of strain and stress are simultaneous, and the slower the effective viscosity, the slower the accumulation rate. We discuss the relationship between the splay faults, fluid, and intraslab earthquakes. Finally, the possibility that effective viscosity may contribute to the mode of occurrence of intraslab earthquakes at the Hikurangi subduction margin is discussed.

Enhanced Neural Network Method-Based Multiscale PCA for Fault Diagnosis: Application to Grid-Connected PV Systems

In this work, an effective Fault Detection and Diagnosis (FDD) strategy designed to increase the performance and accuracy of fault diagnosis in grid-connected photovoltaic (GCPV) systems is developed. The evolved approach is threefold: first, a pre-processing of the training dataset is applied using a multiscale scheme that decomposes the data at multiple scales using high-pass/low-pass filters to separate the noise from the informative attributes and prevent the stochastic samples. Second, a principal component analysis (PCA) technique is applied to the newly obtained data to select, extract, and preserve only the more relevant, informative, and uncorrelated attributes; and finally, to distinguish between the diverse conditions, the extracted attributes are utilized to train the NNs classifiers. In this study, an effort is made to take into consideration all potential and frequent faults that might occur in PV systems. Thus, twenty-one faulty scenarios (line-to-line, line-to-ground, connectivity faults, and faults that can affect the normal operation of the bay-pass diodes) have been introduced and treated at different levels and locations; each scenario comprises various and diverse conditions, including the occurrence of simple faults in the PV1 array, simple faults in the PV2 array, multiple faults in PV1, multiple faults in PV2, and mixed faults in both PV arrays, in order to ensure a complete and global analysis, thereby reducing the loss of generated energy and maintaining the reliability and efficiency of such systems. The obtained outcomes demonstrate that the proposed approach not only achieves good accuracies but also reduces runtimes during the diagnosis process by avoiding noisy and stochastic data, thereby removing irrelevant and correlated samples from the original dataset.

Crustal structure and seismicity characteristics based on dense array monitoring in northwestern Yunnan, China

Owing to the complex geological environment and frequent seismic activity in northwestern Yunnan, China, the northwest Yunnan dense array was installed. The dense array monitored the Mw6.1 Yangbi earthquake that occurred on May 21, 2021, on the western boundary of the Sichuan-Yunnan block. The earthquake exhibited a typical foreshock-mainshock-aftershock sequence and presented a clear process of earthquake migration. From April 1, 2018 to December 31, 2021, 11,623 earthquakes were recorded by 72 stations in the dense array. Based on their arrival time data, the Vp/Vs model consistency-constrained double-difference seismic tomography method was employed. We obtained high-precision crustal 3-D Vp, Vs, Vp/Vs models, and relocation results for northwestern Yunnan. A wide distribution of low-velocity anomalies was observed in the middle and upper crust of northwestern Yunnan. Based on GPS measurements, the contour of the low-Vp zone is aligned with the clockwise movement of crustal material caused by the compression of the Tibetan Plateau. A seismic cluster with a banded distribution pattern was identified in an intersection area of multiple faults. And its formation might be related to the infiltration of geothermal fluid in a fluid channel composed of unknown fractures. The geothermal fluid originates from the intersection of the fault-enriched hot springs. The flow and seepage from the fluid channels spread to the Yangbi earthquake source region, reactivating the previously existing unmapped faults, ultimately leading to the occurrence of Yangbi foreshocks. Because of the stress transmission, the Coulomb stress of the foreshock triggered the high-strength part of the main fault, which triggered the occurrence of the main earthquake.

Integrated system-level prognosis for hybrid systems subjected to multiple intermittent faults

Considering the condition that multiple intermittent faults occur sequentially or simultaneously in hybrid systems, this paper proposes an integrated system-level prognosis scheme. The scheme is built by integrating model-based fault estimation and data-driven system-level remaining useful life (RUL) prediction. As for fault estimation, the level-based learning swarm optimization is utilized to identify state/value changes of intermittently faulty components based on the constructed hybrid system model. With estimation results, the system-level prognosis framework can be designed as follows: 1) The abnormal duration index (ADI) is established to quantify the system degradation extent under multiple intermittent faults. In view of the stochasticity of intermittent fault appearance and disappearance, the ADI is assessed approximately by as pessimistic as possible and as optimistic as possible indicators. 2) A synthetical degradation evaluator is developed to determine the moment to activate predictor, which ensures the obtained fault features can sufficiently reflect the evolutionary trend of the intermittently faulty component when activating RUL predictor. 3) Considering sequential or simultaneous intermittent faults and variations of modes for hybrid systems, the system-level prognosis module is designed, where the optimized support vector regression is adopted for system RUL prediction. Finally, the experimental study is investigated to verify the proposed scheme.

Improved Adaptive Compensation of Variant Fighter With Multiple Faults via Extended Observer

Improved Adaptive Compensation of Variant Fighter With Multiple Faults via Extended Observer

Upper crust anisotropy of the 2020 Jiashi MS 6.4 earthquake

The 2020 Jiashi MS 6.4 earthquake occurred north of the 1997–1998 Jiashi earthquake swarm. Because of its complex tectonic environment and frequent strong earthquake occurrence, scholars have paid extensive attention to this area. To study the upper crustal anisotropy in the source area, we applied microseismic event detection and shear-wave splitting techniques to the seismic data recorded by five stations around the epicenter. First, the earthquake catalog of the Jiashi MS 6.4 earthquake sequence was rebuilt using the “Match &amp;amp; Locate” method. A total of 9,695 earthquake events were obtained, and the number of newly detected earthquakes was approximately 7.3 times the number in the officially released catalog. The newly identified microseismic data greatly increased the number of effective records and improved the reliability of the results. We analyzed shear-wave splitting according to the updated catalog. The results showed that the dominant polarizations of the fast shear waves were in NW or NNW at the stations BPM, XKR, L6505, and L6513, consistent with the stress near the source area. There are also blind faults with an NNW direction in the strike distributing en echelon and parallel to the main stress direction in the Jiashi seismic area. Thus, the fast shear-wave polarization of the four stations may also reflect the strike of multiple buried NNW faults in the study area. The fast shear-wave polarization of station HLJ, located at the Halajun Basin, was E–W, with the overall trend of the Kalpin thrust nappe structure. However, this station didn’t show the same NW or NNW fast-wave direction as the four stations previously mentioned. This finding may indicate that the NW-trending buried faults in the Jiashi seismic area have a limited size in both the length and the depth, only reaching northward near the second row of the Kapingtag nappe structure. The temporal trend of the delay time at station HLJ showed that a stress-release process occurred before the MS 6.4 earthquake and that stress-release occurred again after the mainshock. At station XKR, the delay time rapidly increased and then fell in the early period after the MS 6.4 earthquake, indicating that stress accumulated rapidly after the main earthquake but was released during the aftershock sequence. This study provides novel insights into the complex structural characteristics and seismogenic environment in the Jiashi area.

Anomaly detection of LiFePO4 pouch batteries expansion force under preload force

Overcharging, overheating and other abnormal conditions are important causes of thermal runaway of Li-ion batteries, and a major safety issue for Li-ion batteries. Therefore, it is critical to detect these faults as soon as they are triggered. In this paper, the actual usage scenario of the battery was simulated by setting the appropriate preloading force. And the characteristics of the changes of the battery expansion force under abnormal conditions were studied by battery charging and discharging, overcharging, overdischarging and thermal runaway experiments. In particular, we proposed a fast and high confidence battery fault detection method by the characteristics of the expansion force change under various faults, which could provide early warning of multiple faults. This work provided a reliable method of detecting battery failures at an early stage and ensured process safety and reduced environmental pollution caused by lithium-ion battery accidents through reasonable fire prevention measures.

A generalized gradual fault diagnosis using photovoltaic plant reflectometry profile

This paper presents a generalized gradual fault diagnosis technique for photovoltaic (PV) plants using photovoltaic plant reflectometry profile (PPRP). PV plants can suffer from many types of defects; the most critical are the electrical ones that may appear either after a long or a short period of time. Many fault detection techniques are used depending on fault types, and reflectometry is one of the most efficient. Nevertheless, it has its limitations, such as location detection of the first fault only within a PV string, in addition to not being able to identify the fault type. This paper presents a mathematical model of the reflectometry technique, named hereafter the photovoltaic plant reflectometry profile (PPRP), that enhances its performance by enabling it to detect and locate multiple faults on a N-PV string, in addition to detecting any impedance mismatches, even the smallest ones, between a good PV string and one with faulty modules. Simulations were conducted to evaluate the accuracy of the PPRP analytical model considering a 7-PV string and its effectiveness to detect the expected locations of the faulty modules, and results show that the simulated reflected signal matches exactly the expected PPRP using the developed mathematical model.

Output‐feedback fault‐tolerant tracking control for coupled 2 × 2 hyperbolic PDE systems with multiplicative sensor faults

AbstractThis paper proposes an output‐feedback fault‐tolerant tracking control scheme for coupled hyperbolic PDE systems with multiplicative sensor faults. In order to estimate the system states, an observer is designed by constructing four filters, which impose time delays to their input signals. For sensor fault identification problem, the gradient descent method is utilized based on a parametric model to estimate the fault parameter. With the filter‐based observer and the estimated fault parameters, a novel control strategy is proposed via backstepping design technique to achieve the tracking objective. Finally, the simulation studies are given to verify the tracking performance of the proposed fault‐tolerant tracking control scheme.

Dynamic response characteristics of multiplicative faults in a misaligned-bowed rotor-train system integrated with active magnetic bearings

Dynamic response characteristics of multiplicative faults in a misaligned-bowed rotor-train system integrated with active magnetic bearings

Fault-Tolerant Control of a Variable-Pitch Quadrotor under Actuator Loss of Effectiveness and Wind Perturbations

The actuator fault-tolerant control problem for a variable-pitch quadrotor is addressed under uncertain conditions. Following a model-based approach, the plant nonlinear dynamics are faced with a disturbance observer-based control and a sequential quadratic programming control allocation, where only kinematic data of the onboard inertial measurement unit are required for the fault-tolerant control, i.e., it does not require the measurement of the motor speed nor the current drawn by the actuators. In the case of almost horizontal wind, a single observer handles both faults and the external disturbance. The estimation of the wind is fed forward by the controller, while the actuator fault estimation is exploited in the control allocation layer, which copes with the variable-pitch nonlinear dynamics, thrust saturation, and rate limits. Numerical simulations in the presence of measurement noise show the capability of the scheme to handle multiple actuator faults in a windy environment.

The Single-channel blind source separation based on VMD and Tukey M estimation for rolling bearing composite fault diagnosis

Rolling bearing is one of the core components in rotating machinery, and its running status directly affects the operation of the whole equipment. Faults of rolling bearings in the actual working process are often multiple faults. To effectively separate fault sources, the blind source separation method is used for the compound fault diagnosis of rolling bearings. Because of the impact of the number of artificially limited decompositions and quadratic penalty factor on VMD in the decomposition process, and the slow convergence and low accuracy in the objective function of traditional FastICA operation, the VMD algorithm based on the energy loss coefficient and the information entropy is proposed, which adaptively determines the number of modal components and the quadratic penalty factor; The Tukey M estimation is selected as the objective convergence function of the FastICA algorithm to improve its robustness. First, VMD is used to decompose the signal; Secondly, the original signal and the decomposed IMF component are reconstructed, the covariance matrix and the singular value decomposition are constructed, the number of fault sources is estimated by the proximity dominance method, and the decomposed IMF components are filtered through correlation analysis and kurtosis index to build a multi-channel feature set; Finally, the constructed multi-channel feature set is input to the FastICA algorithm based on the Tukey M estimation for the separation of fault source signals to achieve composite fault diagnosis. The compound fault experiment shows that the proposed method in this paper can effectively realize the blind source separation of rolling bearing fault features to realize the compound fault diagnosis in different positions.

Mechanical Incipient Fault Detection and Performance Analysis Using Adaptive Teager-VMD Method

For large rotating machinery with low speed and heavy load, the incipient fault characteristics of rolling bearings are particularly weak, making it difficult to identify them effectively by direct signal processing methods. To resolve this issue, we propose a novel approach to detecting incipient fault features that combines signal energy enhancement and signal decomposition. First, the structure of a conventional Teager algorithm is modified to further increase the energy of the micro-impact component and hence the impact amplitude. Then, a kind of composite chaotic mapping is constructed to extend the original fruit fly optimization algorithm (FOA) framework, improving the FOA’s randomness and search power. The effective intrinsic mode functions (IMFs) are determined by searching for the optimal combination values of the key parameters of the variational mode decomposition (VMD) with the improved chaotic FOA (ICFOA). The kurtosis index is then used to select the IMFs that are most relevant to the fault characteristics information. Finally, the sensitive components are analyzed to identify multiple early fault characteristics and determine detailed information about the faults. Moreover, the approach is evaluated by a simulation signal and a measured signal. The comprehensive evaluation indicates that the approach has clear advantages over other excellent methods in extracting the incipient fault feature information of the equipment and has great potential for application in engineering.

Saca-FI: A microarchitecture-level fault injection framework for reliability analysis of systolic array based CNN accelerator

As convolutional neural network CNN accelerators are being adopted in emerging safety-critical areas, their reliability becomes prominent. The systolic array is widely used as the major processing structure in CNN accelerators, so its reliability features need to be comprehensively explored. In this work, we develop a microarchitecture-level fault injection framework, saca-FI, to analyze the reliability of systolic array based CNN accelerators. It is able to investigate various vulnerability sources (e.g., single-bit flips, multi-bit flips, stuck-at faults, multiple faults) and at different levels (e.g., model layer and storage locations). Based on saca-FI, we evaluate the resilience of several commonly used networks, and observe several important architecture-level reliability characteristics. We then propose two opportunistic protections to demonstrate the usage of saca-FI in designing energy-efficient error protection mechanisms.