Special issue: Data-driven fault diagnosis of industrial systems

Fault diagnosis (FD) for engineering systems and components has been attracting considerable attention from researchers and engineers in many engineering areas. With the rapid development of Computational Intelligence techniques such as neural networks, fuzzy logic and evolutionary computation, the studies and applications of model-free FD systems have been favourably advanced in the past decades. Such intelligent FD systems have thoroughly detailed the signals and systems methods to describe the dynamics of failure mechanisms in materials, structures, and rotating equipment or even a complex computer program. In the past decades, although model-free FD techniques have been broadly explored in practice, there are still many challenging issues to be further explored in the design of diagnosis systems and algorithms, performance improvement, fault predictability, robust diagnosis of uncertain systems, and framework development for engineering systems. Some new challenges in designing FD systems are also emerging. Some of them are associated with data mining applications, for instance, rule generation and adaptation for rule-based FD expert systems while the discovery of new faults is realized through clustering algorithms and association analysis. This special issue aims to promote the recent advances of data-driven FD systems and algorithms, and address some challenges in designing and developing the state-of-the-art FD systems and algorithms. As a specific application of computing techniques in industrial systems, this special issue has received considerable attention and support from academics, researchers, and engineers. After extensive reviews and revisions, fourteen papers have been accepted. These papers cover a wide variety of applications of fault diagnosis techniques in process industries, for instance, aircraft jet engine and wind turbine diagnosis, case-base reasoning approach for status prediction of a shaft furnace, and multi-sensor data fusion techniques for monitoring system design. It is believed that all papers are of high quality and significance, and such a collection will be useful and valuable to both researchers and engineers. Below, we provide a summary of the papers published in this special issue. The paper entitled ‘‘Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach’’, by Z.N. Sadough Vaninia, K. Khorasania, and N. Meskinb, presents a fault detection and isolation scheme for an aircraft jet engine, where a multiple model approach and dynamic neural networks are employed to accomplish this goal. Using residuals obtained by comparing each network output with the measured jet engine output and by invoking a properly selected threshold for each network, reliable criteria are established for detecting and isolating faults in the jet engine components. The paper entitled ‘‘Bayesian classifiers based on probability density estimation and their applications to simultaneous fault diagnosis’’, by Y.L. He, R. Wang, S. Kwong, and X.Z. Wang, considers a problem of simultaneous fault diagnosis and proposes a new Bayesian classifier for problem solving without assuming the independence among features. Simulation results show that the proposed approach significantly outperforms the traditional ones when the dependence exists among features. The paper entitled ‘‘A fault prediction method that uses improved case-based reasoning to continuously predict the status of a shaft furnace’’, by A.J. Yan, W.X. Wang, C.X. Zhang, and H. Zhao, develops an improved CBR-based fault prediction method for a shaft furnace, where a water-filling theory-based weight allocation model and a group decision-making-based revision model are used for improving the prediction accuracy. Also, the optimal allocation mechanism of channel power and the credibility of historical results are used to enhance the predicted results. The paper entitled ‘‘Improved process monitoring and supervision based on a reliable multi-stage feature-based pattern recognition technique’’, by L. Al-Shrouf, M. Saadawia, and D. Soffker, presents two new multi-sensor data fusion algorithms for object detection in monitoring of industrial processes. The goals of this work are to reduce the rate of false detection and obtain reliable decisions on the presence of target objects. In this work, the classifier is trained and validated by using the real industrial data. The two algorithms are also tested by using the same data, and their performance and modelling complexity are compared.