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There are high expectations for reliability, safety and fault tolerance are high in chemical plants. Control systems are capable of potential faults in the plant processing systems. This paper proposes is a new Fault Tolerant Control (FTC) system to identify the probable fault occurrences in the plant.

A Fault Diagnosis and Isolation (FDI) module has been devised based on the estimated state of system. An Unscented Kalman Filter (UKF) is the main innovation of the FDI module to identify the faults. A Multi-Sensor Data Fusion algorithm is utilized to integrate the UKF output data to enhance fault identification. The UKF employs an augmented state vector to estimate system states and faults simultaneously. A control mechanism is designed to compensate for the undesirable effects of the detected faults.

The performance of the Nonlinear Model Predictive Controller (NMPC) without any fault compensation is compared with the proposed FTC scheme under different fault scenarios. Analysis of the simulation results indicates that the FDI method is able to identify the faults accurately. The proposed FTC approach facilitates recovery of the closed loop performance after the faults have been isolated.

A significant contribution of the paper is the design of an FTC system by using UKF to estimate faults and enhance the accuracy of data. This is done by applying a data fusion algorithm and controlling the system by the NMPC after eliminating the effects of faults.

Considers control systems operating under potentially faulty conditions. Discusses the problem of designing a single unit which not only handles the required control action but also identifies faults occurring in actuators and sensors. In common practice, units for control and for diagnosis are designed separately. Attempts to identify situations in which this is a reasonable approach and cases in which the design of each unit should take the other into consideration. Presents a complete characterization for each case and gives systematic design procedures for both the integrated and non‐integrated design of control and diagnosis units. Shows how a combined module for control and diagnosis can be designed which is able to follow references and reject disturbances robustly, control the system so that undetected faults do not have disastrous effects, reduce the number of false alarms and identify which faults have occurred.

This paper aims to present a novel robust control method based on an adaptive PI controller (APIC) to compensate for different disturbances and unknown dynamics for multi-phase induction machines.

The gains of the APIC are adapted online according to the tracking error. Proposed APIC is accompanied with designed linear disturbance observer (LDO) to present robust behavior to machine parameter variations and fault disturbances.

The results show remarkable dynamic performance in both healthy and faulty conditions when the six-phase induction machine works under APIC and LDO schemes.

The proposed controller need not readjust current controllers for the post-fault condition. The developed Simulink model efficiency is confirmed through experimental tests.

This paper deals with the analysis, the modeling, the control and the fault‐tolerance capability of a three‐switch inverter (TSI, also known delta‐inverter) fed fractional‐slot six‐phase brushless DC motor (BDCM) drive.

Following the presentation of the advantages of multi‐phase fractional‐slot brushless machines and the possibility of their association to TSI, the analysis of the operating sequences as well as the modeling of a TSI fed six‐phase BDCM drive are developed. Then, a dedicated control strategy of such a drive is synthesized. Finally, a case study is simulated considering both transient behaviour during the start‐up of the BDCM as well as a steady‐state one under healthy and faulty operations.

It has been found that the 60‐electrical degree shift between the six phases of the BDCM makes it simple to achieve its operating sequences with its armature fed by a TSI, considering a suitable anti‐parallel connection of the six phases.

Crucial cost benefits associated with improved compactness, reliability, and fault‐tolerance capability could be gained thanks to the integration of TSI fed six‐phase BDCM drives in large‐scale production industries, such as the automotive one.

The paper proposes an analysis of the operating sequences as well as the fault‐tolerance capability of TSI fed six‐phase BDCM drives.

The purpose of this paper is to describe a proposed framework for traceability purpose. Hence, the framework provides a formal and structured way of viewing a traceability solution. This structure lays the required bases for a traceability system before starting development and deployment.

The paper examines several traceability publications, including systems and literature review. The study covers the traceability implementation phase. Therefore, this research approaches the traceability issue from three perspectives (description, engineering and executive one). The separation between aspects is essential when describing and comparing traceability systems. This distinction is also helpful when recommending solution improvements.

The framework identifies six traceability bases: aims, functions, specifications, data classification, processes and procedures. These can establish a basis for a general purpose tool that can enable users to develop an efficient traceability solution. Thus, the first ontology expresses the framework domain and ensures optimal use of it. The second one represents the bases that can serve as a knowledge base to manage the product data.

The suggested framework tackles the implementation of traceability. Therefore, the design emphasizes the importance of technological concerns. Some studied cases could require more research angles (i.e. economic and legislative). Thus, framework enrichment is essential for further improvements.

The framework helps users to develop a general, interoperable and scalable traceability solution. These are important to promote the generalization of traceability systems.

The framework fulfills a requirement for establishing general traceability foundations. Therefore, the guide independently operates of the product or the industry specificity. Moreover, the bases aim to bridge the gap between solution engineering and traceability requirements.

This paper aims to clarify voltage sourced converter’s (VSC’s) influence rules on the alternating current (AC) short-circuit current and identify the key factors, so as to propose the short-circuit current suppression strategy.

This paper investigates the key factors which impact the short-circuit current supplied by the VSC based on the equivalent current source model. This study shows that the phase of the VSC equivalent current source is mainly affected by the type of fault, whereas the amplitude is mainly decided by the control mode, the amplitude limiter and the electrical distance. Based on the above influence mechanism, the dynamic limiter with short-circuit current limiting function is designed. The theoretical analysis is verified by simulations on PSCAD.

The short-circuit current feeding from VSC is closely related to the control mode and control parameters of the VSC, fault type at AC side and the electrical distance of the fault point. The proposed dynamic limiter can make VSC absorb more reactive power to suppress the short-circuit current.

The dynamic limiter proposed in this paper is limited to suppress three-phase short-circuit fault current. The future work will focus more on improving and extending the dynamic limiter to the fault current suppression application in other fault scenarios.

The research results provide a reference for the design of protection system.

The key influence factors are conducive to put forward the measures to suppress the fault current, eliminate the risk of short-circuit current exceeding the standard and reduce the difficulty of protection design.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.