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

Business intelligence (BI) is the process of transforming raw data into useful information for more effective strategic, operational insights, and decision-making purposes so that it yields real business benefits. This new emerging technique can not only improve applications in enterprise systems and industrial informatics, respectively, but also play a very important role to bridge the connection between enterprise systems and industrial informatics. This paper was intended as a short introduction to BI with the emphasis on the fundamental algorithms and recent progress. In addition, we point out the challenges and opportunities to smoothly connect industrial informatics to enterprise systems for BI research.

Business Intelligence for Enterprise Systems: A Survey

In this paper, a data-driven scheme of key performance indicator (KPI) prediction and diagnosis is developed for complex industrial processes. For static processes, a KPI prediction and diagnosis approach is proposed in order to improve the prediction performance. In comparison with the standard partial least squares (PLS) method, the alternative approach significantly simplifies the computation procedure. By means of a data-driven realization of the so-called left coprime factorization (LCF) of a process, efficient KPI prediction, and diagnosis algorithms are developed for dynamic processes, respectively, with and without measurable KPIs. The proposed KPI prediction and diagnosis scheme is finally applied to an industrial hot strip mill, and the results demonstrate the effectiveness of the proposed scheme.

A Novel Scheme for Key Performance Indicator Prediction and Diagnosis With Application to an Industrial Hot Strip Mill

“Big data” is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.

Recent Development in Big Data Analytics for Business Operations and Risk Management

In prognostics approaches, features (e.g., vibration level, root mean square or outputs from signal processing techniques) extracted from the measurement (e.g., vibration, current, and pressure, etc.) are often used or modeled as an indicator to the equipment's health condition. When faults are detected or when increasing/decreasing trends are shown in the health indicator, prediction algorithms are applied to extrapolate the future behavior and predict remaining useful life (RUL). However, it is difficult to make an accurate prediction if the trend of the health indicator is not obvious through the entire life cycle or if the trend is only shown right before a failure occurs. The challenge lies in whether an advanced feature (e.g., a mathematical combination of a group of the extracted features) can be found to clearly present/correlate with the fault progression. A genetic programming method is proposed to address the challenge of automatically discovering advanced feature(s), which can well capture the fault progression, from the measurement or extracted features in the purpose of RUL prediction.

Discovering Prognostic Features Using Genetic Programming in Remaining Useful Life Prediction

This paper presents a robust and efficient method to discover knowledge for classification problems through data summarization. It discretizes continuous features and then summarizes the data using a contingency table. Inconsistency rate for different subsets of features can then be easily calculated from the contingency table. Sequential search is then used to find the best feature subset. After the number of features is reduced to a certain extent, easy-to-understand knowledge can be intuitively derived from data summary. Another desirable feature of the proposed method is its capability to learn incrementally; namely, knowledge can be updated quickly whenever new data are obtained. Moreover, the proposed method is capable of handling missing values when used for prediction. The method is applied on two benchmark data sets showing its effectiveness on selecting discriminative features. The practical usefulness of this method in manufacturing is demonstrated through an application on welding fault diagnosis.

S.H. Huang

Papers

Robust and Efficient Rule Extraction Through Data Summarization and Its Application in Welding Fault Diagnosis