Special issue on recent advances on control and diagnosis via process measurements

The recent increasing amounts of process measurements produced in various complex applications have demonstrated a “data driven” epoch of modern industrial processes. With the rapid developments of information science and technology, both the advanced data storage devices and the fast data transmission equipments have promoted the efficient processing of big data into realization. As a result, available process measurements can be applied to improving the effectiveness of current methodologies or practical techniques related to various subjects of modern industrial application research. Compared to the well-established model based techniques in the last few decades, the recent developments on control and diagnosis via available process measurements have received more attention both from academic and practical domains. The common object of data driven approaches is the effective utilization of considerable amounts of measured or stored data to achieve regular running and desired performance of modern industrial applications. The primary objective of this special issue is to provide an international forum for researchers and practitioners to exchange their latest achievements and to identify critical issues, challenges and emerging trends for future investigation of data based techniques. This special issue presents high-quality articles describing: modeling and key parameter identification for complicated systems, data based fault diagnosis and fault tolerant control, recent advances on filtering and control via process measurement. We received a total of 23 submissions, and all submitted papers have been carefully reviewed after a rigorous review process. We selected 11 articles covering the subject from different perspectives, i.e., 47.8% of all the submitted papers. The first contribution, entitled “Bearing fault diagnosis with morphological gradient wavelet” by Mohammad H Khakipour, Ali A Safavi and Peyman Setoodeh, focuses on morphological wavelet transform for bearing fault diagnosis. This paper proposes a morphological gradient wavelet scheme in order to extract impulsive features and perform noise reduction in the vibration signals of defective bearings. Vibration signals of two defective bearings are investigated, one with an inner race fault and the other with an outer race fault. The proposed morphological gradient wavelet algorithm owns advantaged speed and simplicity of implementation. Therefore, it is suitable for real-time signal processing aimed at online condition monitoring. The second paper “Stochastic degradation process modeling and remaining useful life estimation with flexible random-effects” by Zhengxin Zhang, Changhua Hu and Xiao-Sheng Si