This paper provides an overview and analysis of statistical process monitoring methods for fault detection, identification and reconstruction. Several fault detection indices in the literature are analyzed and unified. Fault reconstruction for both sensor and process faults is presented which extends the traditional missing value replacement method. Fault diagnosis methods that have appeared recently are reviewed. The reconstruction-based approach and the contribution-based approach are analyzed and compared with simulation and industrial examples. The complementary nature of the reconstruction- and contribution-based approaches is highlighted. An industrial example of polyester film process monitoring is given to demonstrate the power of the contribution- and reconstruction-based approaches in a hierarchical monitoring framework. Finally we demonstrate that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring. Additional topics are summarized at the end of the paper for future investigation. Copyright © 2003 John Wiley & Sons, Ltd.

Statistical process monitoring: basics and beyond

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

Survey on data-driven industrial process monitoring and diagnosis

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

Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic. First, the natures of different industrial processes are revealed with their data characteristics analyzed. Second, detailed terminologies of the data-based process monitoring method are illustrated. Third, based on each of the main data characteristics that exhibits in the process, a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods. Finally, the relevant research perspectives and several promising issues are highlighted for future work.

Review of Recent Research on Data-Based Process Monitoring

A chemical process has a large number of measured variables, but it is usually driven by fewer essential variables, which may or may not be measured. Extracting such essential variables and monitoring them will improve the process-monitoring performance. Independent component analysis (ICA) is an emerging technique for finding several independent variables as linear combinations of measured variables. In this work, a new statistical process control method based on ICA is proposed. For investigating the feasibility of its method, its fault-detection performance is evaluated and compared with that of the conventional multivariate statistical process control (cMSPC) method using principal-component analysis by applying those methods to monitoring problems of a simple four-variable system and a continuous-stirred-tank-reactor process. The simulated results show the superiority of ICA-based SPC over cMSPC.

Monitoring independent components for fault detection

Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem

A new multivariate statistical process monitoring method using principal component analysis

Evolution of multivariate statistical process control: application of independent component analysis and external analysis

Multivariate statistical process control (MSPC) has been widely used for monitoring chemical processes with highly correlated variables. In this work, a novel statistical process monitoring method is proposed based on the idea that a change of operating condition can be detected by monitoring a distribution of process data, which reflects the corresponding operating conditions. To quantitatively evaluate the difference between two data sets, a dissimilarity index is introduced. The monitoring performance of the proposed method, referred to as DISSIM, and that of the conventional MSPC method are compared with their applications to simulated data collected from a simple 2 × 2 process and the Tennessee Eastman process. The results clearly show that the monitoring performance of DISSIM, especially dynamic DISSIM, is considerably better than that of the conventional MSPC method when a time-window size is appropriately selected.

Hiromu Ohno

Papers

Monitoring independent components for fault detection

Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem

A new multivariate statistical process monitoring method using principal component analysis

Evolution of multivariate statistical process control: application of independent component analysis and external analysis

Statistical process monitoring based on dissimilarity of process data