An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

• Published in: Computers in industry 2015-12, Vol.74, p.75-94
• Main Authors: Precup, Radu-Emil, Angelov, Plamen, Costa, Bruno Sielly Jales, Sayed-Mouchaweh, Moamar
• Format: Article
• Language: English
• Subjects: Complex systems; Controllers; Data mining; Data-driven control; Dynamical systems; Evolving soft computing techniques; Fault diagnosis; Mathematical analysis; Mathematical models; Nature-inspired optimization algorithms; Optimal control; Optimization; Optimization algorithms; Studies; Tuning; Wind turbines
• ISSN: 0166-3615; 1872-6194
• DOI: 10.1016/j.compind.2015.03.001

•An overview on recent developments in fault diagnosis is carried out.•Machine learning, data mining and evolving soft computing techniques are discussed.•Real liquid level control, wind turbine and servo system applications are offered.•An overview on nature-inspired optimal control of industrial processes is given.•New research challenges with strong industrial impact are highlighted. Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.