With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

/pdf/a-survey-of-fault-diagnosis-and-fault-tolerant-techniques-9uuf1uqysf.pdf

A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Statistics and Data Analysis in Geology

Deep learning and its applications to machine health monitoring

Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

This paper provides an overview of the recent developments in data-based techniques focused on modern industrial applications. As one of the hottest research topics for complicated processes, the data-based techniques have been rapidly developed over the past two decades and widely used in numerous industrial sectors nowadays. The core of data-based techniques is to take full advantage of the huge amounts of available process data, aiming to acquire the useful information within. Compared with the well-developed model-based approaches, data-based techniques provide efficient alternative solutions for different industrial issues under various operating conditions. The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view. This paper begins with a brief evolutionary overview of data-based techniques in the last two decades. Then, the methodologies only based on process measurements and the model-data integrated techniques will be further introduced. The recent developments for modern industrial applications are, respectively, presented mainly from perspectives of monitoring and control. The new trends of data-based technique as well as potential application fields are finally discussed.

Data-Based Techniques Focused on Modern Industry: An Overview

In this paper, two online schemes for an integrated design of fault-tolerant control (FTC) systems with application to Tennessee Eastman (TE) benchmark are proposed. Based on the data-driven design of the proposed fault-tolerant architecture whose core is an observer/residual generator based realization of the Youla parameterization of all stabilization controllers, FTC is achieved by an adaptive residual generator for the online identification of the fault diagnosis relevant vectors, and an iterative optimization method for system performance enhancement. The performance and effectiveness of the proposed schemes are demonstrated through the TE benchmark model.

Real-Time Implementation of Fault-Tolerant Control Systems With Performance Optimization

Standard partial least squares (PLS) serves as a powerful tool for key performance indicator (KPI) monitoring in large-scale process industry for last two decades. However, the standard approach and its recent modifications still encounter some problems for fault diagnosis related to KPI of the underlying process. To cope with these difficulties, an improved PLS (IPLS) approach is presented in this paper. IPLS is able to decompose the measurable process variables into the KPI-related and unrelated parts, respectively. Based on it, the corresponding test statistics are designed to offer meaningful fault diagnosis information and thus, the corresponding maintenance actions can be further taken to ensure the desired performance of the systems. In order to demonstrate the effectiveness of the proposed approach, a numerical example and Tennessee Eastman (TE) benchmark process are respectively utilized. It can be seen that the proposed approach shows satisfactory results not only for diagnosing KPI-related faults but also for its high fault detection rate.

Shen Yin

Papers

A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

Data-Based Techniques Focused on Modern Industry: An Overview

Real-Time Implementation of Fault-Tolerant Control Systems With Performance Optimization

Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis