Change point and fault detection using Kantorovich Distance

Kammammettu, Sanjula; Li, Zukui

doi:10.1016/j.jprocont.2019.05.012

Cited by 28 publications

(13 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The KD metric KDA was computed for Ru1 and Ru2. Next, the threshold α was computed using the following expression [39]:…”

Section: A Kd Statistic Thresholdmentioning

confidence: 99%

“…The KD metric indicates the minimum cost to shift a mass of data from source to destination distribution. The KD metric has been integrated with PCA modeling framework and the following advantages were observed: KD metric provided a smooth transition of faults, it detected the faults of small magnitude and provided good monitoring for data corrupted with noise [39]. The KD metric has been utilized for change point detection problem where it yielded good results with a minimum detection delay [40].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes

2022

View full text Add to dashboard Cite

Fault detection is necessary for safe operation in modern process plants. The kernel principal component analysis (KPCA) technique has been widely utilized for monitoring non-linear processes because it enhances dimension reduction and fault detection in non-linear space. In this paper, an improved nonlinear fault detection strategy based on Kantorovich distance (KD) and kernel principal component analysis is proposed. The KD statistic is based on the optimal mass transport theory where the distance between two distributions is computed with respect to a cost function. The addressed fault detection problem models the data using the KPCA framework and utilizes the ability of the KD statistical indicator to detect faults. The detection stage involves comparing the residuals of training fault-free data and testing faulty data using the KD statistic. Additionally, the reference threshold for the KD statistic is computed using the kernel density estimation (KDE) approach as compared to the previously utilized three-sigma rule approach. The detection performance is illustrated with the help of three benchmark case studies: a continuous stirred tank reactor (CSTR) process, Tennessee Eastman (TE) process and an experimental distillation column process. The performance analysis suggests the superiority of the KPCA-KD fault detection scheme in monitoring various sensor faults. Moreover, comparison with traditional statistical indicators of PCA and KPCA schemes shows that the proposed scheme enhances fault detection and achieves an improved detection rate in monitoring different categories of faults.INDEX TERMS Kantorovich distance, kernal principal component analysis, continuous stirred tank reactor process, Tennessee Eastman process, experimental distillation column process, fault detection.

show abstract

“…The KD metric KDA was computed for Ru1 and Ru2. Next, the threshold α was computed using the following expression [39]:…”

Section: A Kd Statistic Thresholdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes

2022

View full text Add to dashboard Cite

show abstract

“…A drawback of this method is experiencing a high rate of either false or missed alarms (or more precisely, chattering alarms). A solution to this problem is using some nonlinear filters such as those proposed by refs , , .…”

Section: Alarm Performance Indexmentioning

confidence: 99%

Control and Alarm Interplay and Robust State-Feedback Synthesis with an Alarm Performance Constraint

Roohi

Chen

Izadi

2020

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

In this paper, the effect of control systems, as a missing (but significant) actor, on the performance of alarm systems is studied. The interplay of control and alarm performance is justified, and it is shown that a state-feedback controller, which is designed to achieve a good quality of control (in terms of minimum output variance), has both desirable and undesirable consequences on the performance of alarm systems. To pave the path for finding a robust controller solution, a new alarm index is introduced and an analytical expression of the performance is derived for signals, known systems, and systems with parameter uncertainties. A set of linear matrix inequalities (LMIs) is proposed for the controller design to satisfy the required control and alarm performance. The effectiveness of the method is illustrated through a case study of a heat exchanger network, which is a frequently used plant in petrochemical industries.

show abstract

“…In ICA fault detection strategy and its variant schemes, the monitoring of new process data is carried out with help of three fault detection indicies: I The KD metric, which has its roots from optimal mass transport theory, measures distance between two distributions and uses this distance as a measure of fault. It has been observed in the work by [24] that KD metric has provided very good monitoring results in presence of heavy measurement noise and also offered better detection of faults with small magnitude. The KD metric has been employed for change point detection problem where it yielded improved results with lesser false alarms and missed alarms [25].…”

Section: Introductionmentioning

confidence: 99%

Improved Process Monitoring Strategy Using Kantorovich Distance-Independent Component Analysis: An Application to Tennessee Eastman Process

Kini

Madakyaru

2020

IEEE Access

View full text Add to dashboard Cite

Vowing to the increasing complexity in industrial processes, the need for safety is of highest priority and this has led to development of efficient fault detection (FD) methods. Also, with rapid development of data acquisition systems, process history based methods have gained importance as their dependency is on large volume of sensor data extracted from the process. The industrial data exhibits some degree of non-gaussianity for which Independent Component Analysis (ICA) technique has usually been applied in practice. Recently, a new fault indicator based on Kantorovich Distance (KD) has been proposed which computes distance between two distributions and uses the distance as an indicator of fault. The KD metric has found to provide good monitoring results for data in presence of noise and offers enhanced detection of small magnitude faults. Considering the benefits offered by KD metric, the objective of this work is to amalgamate KD metric with ICA modeling framework to have a fault detection strategy that can improve process monitoring in noisy environment. The proposed ICA-KD FD strategy is illustrated on four processes that includes Modified Continuous Stirred Tank Heater (CSTH), Tennessee Eastman (TE) process and Experimental Distillation Column Process. The simulation results indicate that the proposed FD strategy exhibits improved performance over conventional strategies while monitoring different sensor faults in noisy environment.INDEX TERMS Process monitoring, fault detection, independent component analysis, Kantorovich distance, small magnitude faults, Tennessee Eastman process, experimental distillation column process, modified continuous stirred tank heater process.

show abstract

Change point and fault detection using Kantorovich Distance

Cited by 28 publications

References 16 publications

Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes

Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes

Control and Alarm Interplay and Robust State-Feedback Synthesis with an Alarm Performance Constraint

Improved Process Monitoring Strategy Using Kantorovich Distance-Independent Component Analysis: An Application to Tennessee Eastman Process

Contact Info

Product

Resources

About