On-Line Fault Diagnosis and Fault-Tolerant Operation of Modular Multilevel Converters –A Comprehensive Review

Modular multilevel converters(MMCs)have been one of the most broadly used multilevel converter topologies in industrial applications,particularly in medium-voltage motor drives and high-voltage dc power conversion systems.However,due to the utilization of large amount of semiconductor devices,the reliability of MMCs becomes one of the severe challenges constraining their further development and applications.In this paper,common electrical faults of the MMC have been summarized and analyzed,including open-circuit switching faults,short-circuit switching faults,dc-bus short-circuit faults,and single line-to-ground faults on the ac side.A thorough and comprehensive review of the existing online fault diagnostic methods has been conducted.In addition,fault-tolerant operation strategies for such various fault scenarios in MMCs have been presented.All the fault diagnosis and fault-tolerant operation strategies are comparatively evaluated,which aims to provide a state-of-the-art reference on the MMC reliability for future research and industrial applications.

Fault diagnosis and fault-tolerant control of photovoltaic micro-inverter

An observer-based fault diagnosis method and a fault tolerant control for open-switch fault and current sensor fault are proposed for interleaved flyback converters of a micro-inverter system. First, based on the topology of a grid-connected micro-inverter, a mathematical model of the flyback converters is established. Second, a state observer is applied to estimate the currents online and generate corresponding residuals. The fault is diagnosed by comparing the residuals with the thresholds. Finally, a fault-tolerant control that consists of a fault-tolerant topology for the faulty switch and a simple software redundancy control for the faulty current sensor, is proposed to achieve a fault-tolerant operation. The feasibility and effectiveness of the proposed method has been verified by simulation and experimental results.

Design and Analysis of Fault Diagnosis and Fault-tolerant Control for a Class of MIMO Nonlinear State Systems

This paper presents a fault diagnosis and fault-tolerant control algorithm,which can be used for a class of multi-input multi-output(MIMO)nonlinear state systems.First,a state estimator is proposed,which is able to detect fault occurrence,by using a residual signal.Second,when the state is at an abnormal condition,the fault-tolerant control will be triggered to minimize the impact of the fault occurrence.This fault-tolerant control is designed by using a robust controller(original controller),and an on-line approximator to capture a nonlinear function that indicates the fault occurrence.The detailed analysis is given for the proposed fault accommodation control.

Rule-based Fault Diagnosis of Hall Sensors and Fault-tolerant Control of PMSM

Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.

Temperature fault-tolerant control system of CSTR with coil and jacket heat exchanger based on dual control and fault diagnosis

For the characteristics of the continuous stirred-tank reactor(CSTR) with coil and jacket cooling system,a CSTR temperature dual control solution based on the analysis of the CSTR exothermic reaction control characteristic was proposed for an organic material polymerization production.The control solution has passive fault-tolerant ability for the jacket cooling water cutting off fault and active fault-tolerant potential for the coil cooling water cutting off fault,and it has good control ability,high saving energy and reducing consumption performance.Fault detection and diagnosis and fault-tolerant control strategy are designed for the coil cooling fault to achieve the active fault-tolerant control function.The CSTR temperature dual control,process fault detection and diagnosis and active fault-tolerant control were full integrated into the CSTR temperature fault-tolerant control system,which achieve fault tolerance control of CSTR temperature for any severe malfunction of jacket cooling or coil cooling cutting off,and the security for CSTR exothermic reaction is improved.Finally,the effectiveness of this system was validated by semi-physical simulation experiment.

CALL FOR PAPERS Special Section on New Developments on Fault Detection and Diagnosis(FDD)and Fault-Tolerant Control(FTC)Techmiques

The increased complexity in system and more needs of advanced and complicated actuators and sensors to-wards higher level of automation and autonomy in different engineering applications render the engineering systems susceptible to faults,which may significantly degrade the control performance or even cause cata-strophic accidents.Recently,fault detecticn and diagnosis(FDD)and fault-tolerant control(FTC)techniques are widely used to diagnose and tclerant the actuatar/sensor faults,respectively.

New Developments on Fault Detection and Diagnosis (FDD) and Fault-Tolerant Control (FTC) Techniques

The increased complexity in system and the growing need of advanced and complicated actuators and sensors towards higher level of automation and autonomy in different engineering applications render the engineering systems susceptible to faults,which may significantly degrade the control performance or even cause catastrophic accidents.Recently,fault detection and diagnosis(FDD)and fault-tolerant control(FTC)techniques have been widely used to diagnose and compensate for the actuator/sensor faults,respectively.

Fault Diagnosis and Phase Disposition Reconfiguration of a 5-Level Double-Boost Power Factor Corrector with Fault-Tolerant Capability

A 5-level PFC （power factor correction） topology with fault-diagnostic and fault-tolerant capability is proposed and compared to known structures. It is derived from a 3-level non differential double-boost PFC including fly-cap cells. The series-connection of the two low-voltage switching-cells is decoupled by a single flying capacitor that provides a direct fault-tolerant capability and a post-failure operation increasing the availability of converter. The monitoring of the voltages across flying capacitors allows a rapid detection and localization either for open circuit failure or short-circuits failure. A PWM （pulse width modulation） phase-disposition type reconfiguration is also used and presented in order to optimize both normal operation and post-fault continuation. The design and the most important features are highlighted thanks to a digital control frame and a mock-up rated to： AC voltage network 115 V-load 400 V-nominal power 4 kW-switching frequency 62 kHz.

Dynamic Vision Enabled Contactless Cross-Domain Machine Fault Diagnosis With Neuromorphic Computing

Dear Editor,This letter presents a novel dynamic vision enabled contactless cross-domain fault diagnosis method with neuromorphic computing.The event-based camera is adopted to capture the machine vibration states in the perspective of vision.

A novel multi-resolution network for the open-circuit faults diagnosis of automatic ramming drive system

The open-circuit fault is one of the most common faults of the automatic ramming drive system(ARDS),and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor(PMSM)and the open-circuit faults of Voltage Source Inverter(VSI). The stator current serves as a common indicator for detecting open-circuit faults. Due to the identical changes of the stator current between the open-phase faults in the PMSM and failures of double switches within the same leg of the VSI, this paper utilizes the zero-sequence voltage component as an additional diagnostic criterion to differentiate them.Considering the variable conditions and substantial noise of the ARDS, a novel Multi-resolution Network(Mr Net) is proposed, which can extract multi-resolution perceptual information and enhance robustness to the noise. Meanwhile, a feature weighted layer is introduced to allocate higher weights to characteristics situated near the feature frequency. Both simulation and experiment results validate that the proposed fault diagnosis method can diagnose 25 types of open-circuit faults and achieve more than98.28% diagnostic accuracy. In addition, the experiment results also demonstrate that Mr Net has the capability of diagnosing the fault types accurately under the interference of noise signals(Laplace noise and Gaussian noise).

Expert Experience and Data-Driven Based Hybrid Fault Diagnosis for High-SpeedWire Rod Finishing Mills

The reliable operation of high-speed wire rod finishing mills is crucial in the steel production enterprise.As complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault location and real-time monitoring.To solve the above problems,an expert experience and data-driven-based hybrid fault diagnosis method for high-speed wire rod finishing mills is proposed in this paper.First,based on its mechanical structure,time and frequency domain analysis are improved in fault feature extraction.The approach of combining virtual value,peak value with kurtosis value index,is adopted in time domain analysis.Speed adjustment and side frequency analysis are proposed in frequency domain analysis to obtain accurate component characteristic frequency and its corresponding sideband.Then,according to time and frequency domain characteristics,fault location based on expert experience is proposed to get an accurate fault result.Finally,the proposed method is implemented in the equipment intelligent diagnosis system.By taking an equipment fault on site,for example,the effectiveness of the proposed method is illustrated in the system.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

A finite-time fuzzy adaptive output-feedback fault-tolerant control for underactuated wheeled mobile robots systems

This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. The UWMR system includes unknown nonlinear dynamics and immeasurable states. Fuzzy logic systems(FLSs) are utilized to work out immeasurable functions. Furthermore, with the support of the backsteppingcontrol technique and adaptive fuzzy state observer, a fuzzy adaptive finite-time output-feedback FTC scheme isdeveloped under the intermittent actuator faults. It is testifying the scheme can ensure the controlled nonlinearUWMRs is stable and the estimation errors are convergent. Finally, the comparison results and simulationvalidate the effectiveness of the proposed fuzzy adaptive finite-time FTC approach.

Research on Rotating Machinery Fault Diagnosis Based on Improved Multi-target Domain Adversarial Network

Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model(WDMACN)and Gram Angle Product field(GAPF)was proposed.Firstly,the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series.Secondly,the residual network is used to extract data features,and the features of the target domain without labels are pseudo-labeled,and the transferable features among the feature extractors are shared through the depth parameter,and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish.The modelt through adversarial domain adaptation,thus achieving fault classification.Finally,a large number of validations were carried out on the bearing data set of Case Western Reserve University(CWRU)and the gear data.The results show that the proposed method can greatly improve the diagnostic efficiency of the model,and has good noise resistance and generalization.

Causal temporal graph attention network for fault diagnosis of chemical processes

Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical processes.However,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their decisions.In this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of faults.The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations.Experiments in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability.

Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.

Analysing Recent Breakthroughs in Fault Diagnosis through Sensor:A Comprehensive Overview

Sensors,vital elements in data acquisition systems,play a crucial role in various industries.However,their exposure to harsh operating conditions makes them vulnerable to faults that can compromise system performance.Early fault detection is therefore critical for minimizing downtime and ensuring system reliability.This paper delves into the contemporary landscape of fault diagnosis techniques for sensors,offering valuable insights for researchers and academicians.The papers begin by exploring the different types and causes of sensor faults,followed by a discussion of the various fault diagnosis methods employed in industrial sectors.The advantages and limitations of these methods are carefully examined,paving the way for highlighting current challenges and outlining potential future research directions.This comprehensive review aims to provide a thorough understanding of current advancements in sensor fault diagnosis,enabling readers to stay abreast of the latest developments in this rapidly evolving field.By addressing the challenges and exploring promising research avenues,this paper seeks to contribute to the development of more robust and effective sensor fault diagnosis methods,ultimately improving the reliability and safety of industrial and agricultural systems.

Dynamic Vision-Based Machinery Fault Diagnosis With Cross-Modality Feature Alignment

Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades,and the vibration acceleration data collected by contact accelerometers have been widely investigated.In many industrial scenarios,contactless sensors are more preferred.The event camera is an emerging bio-inspired technology for vision sensing,which asynchronously records per-pixel brightness change polarity with high temporal resolution and low latency.It offers a promising tool for contactless machine vibration sensing and fault diagnosis.However,the dynamic vision-based methods suffer from variations of practical factors such as camera position,machine operating condition,etc.Furthermore,as a new sensing technology,the labeled dynamic vision data are limited,which generally cannot cover a wide range of machine fault modes.Aiming at these challenges,a novel dynamic vision-based machinery fault diagnosis method is proposed in this paper.It is motivated to explore the abundant vibration acceleration data for enhancing the dynamic vision-based model performance.A crossmodality feature alignment method is thus proposed with deep adversarial neural networks to achieve fault diagnosis knowledge transfer.An event erasing method is further proposed for improving model robustness against variations.The proposed method can effectively identify unseen fault mode with dynamic vision data.Experiments on two rotating machine monitoring datasets are carried out for validations,and the results suggest the proposed method is promising for generalized contactless machinery fault diagnosis.

Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art performance.However,the deep learning model is often not truly trusted by users due to the lack of interpretability of“black box”,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-making loop can be found to deal with the abnormal situa-tion when the models fail.In this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called SAEU.In SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural networks.Based on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault diagnosis.Moreover,our UU-DLF effectively embodies the idea of“humans in the loop”,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also makes correspond-ing improvements on existing models based on traceability analy-sis.Finally,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.

Intelligent Diagnosis Method for Typical Co-frequency Vibration Faults of Rotating Machinery Based on SAE and Ensembled ResNet-SVM

Intelligent fault diagnosis is an important method in rotating machinery fault diagnosis and equipment health management.To deal with co-frequency vibration faults,a type of typical fault in rotating machinery,this paper proposes a fault diagnosis method based on the stacked autoencoder(SAE)and ensembled ResNet-SVM.Furthermore,the time-and frequency-domain features of several co-frequency vibration faults are summarized based on the mechanism analysis and calculated using actual vibration data.To realize and validate the high-precision diagnosis method of rotating equipment with co-frequency faults proposed in this study,the following three criteria are required:First,to improve the effectiveness and robustness of the ensembled model and the sliding window using data augmentation,adding noise,autoencoder(AE)and SAE methods are analyzed in terms of principle and practical effects.Second,ResNet is used as the feature extractor for the ensembled ResNet-SVM model.Feature extraction is carried out twice,and the extracted co-frequency fault features are more comprehensive.Finally,the data augmentation method and ensemble ResNet-SVM are combined for fault diagnosis and compared with other methods.The experimental results show that the accuracy of the proposed method can exceed 99.9%.