A B-spline based method for data compression, process monitoring and diagnosis

Vedam, Hiranmayee; Venkatasubramanian, Venkat; Bhalodia, Mohan

doi:10.1016/s0098-1354(98)00158-6

Cited by 23 publications

(16 citation statements)

References 6 publications

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“…Rengaswamy and Venkatasubramanian (1995) have shown how primitives can be extracted from raw noisy sensor data by treating the problem of primitive identification as a classification problem using neural networks. Vedam and Venkatasubramanian (1997) proposed a wavelet theory based adaptive trend analysis framework and later proposed a dyadic B-Spline based trend analysis algorithm (Vedam et al, 1998). Recently, Rengaswamy et al (2001) have discussed the utility of trend modeling in control loop performance assessment.…”

Section: Process History-based Methodsmentioning

confidence: 99%

“…There are a number of papers that address the problem of fault detection and diagnosis using back-propagation neural networks. In chemical engineering, Venkatasubramanian (1985), Watanabe et al (1989) and Venkatasubramanian and Chan (1989), and Hoskins et al (1991) were among the early researchers to demonstrate the usefulness of neural networks for fault diagnosis. Later, a more detailed and thorough analysis of the learning, recall and generalization characteristics of neural networks was presented by Venkatasubramanian et al (1990) and Vaidhyanathan and Venkatasubramanian (1992).…”

Section: Process History-based Methodsmentioning

confidence: 99%

“…A blackboard-based cooperative problem-solving framework where different diagnostic methods work in conjunction to perform collective process fault diagnosis has been proposed by (Mylaraswamy, 1996;Mylaraswamy and Venkatasubramanian, 1997;Vedam and Venkatasubramanian, 1999). The blackboard architecture, called Dkit, in which different diagnostic methods analyze the same problem, and a scheduler which regulates the decision-making of these methods, is the central concept in this framework.…”

Section: A Comparison Of Various Approachesmentioning

confidence: 99%

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Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities

Venkatasubramanian

2005

Computers & Chemical Engineering

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Modern technological advances have resulted in a myriad of complex systems, processes and products. These increasingly complicated processes, systems and products pose considerable challenges in their design, analysis, manufacturing and management for successful operation and use over their life cycles. In the process industries, for example, the maintenance and management of complex process equipment and processes, and their integrated operation, play a cruicial role in ensuring the safety of plant personnel and the environment as well as the timely delivery of quality products. In the discrete parts industries, such as the auto industry, many product malfunctions are due to unanticipated dynamic interactions, due to repeated use or misuse of components. These interactions thrive in complex systems when the combined effects of uncertainty and operational adversity are not properly addressed either in design or in operation. Given the size, scope, and complexity of the systems and inretactions it is becoming increasingly difficult for plant personnel to anticipate, diagnose and control serious abnormal events in a timely manner. Hence, it should come as no surprise that human operators tend to make erroneous decisions and take actions which make matters even worse, as reported in the literature. All these cost the companies and consumers in billions of dollars every year in product lifecycle management. Businesses and federal organizations are increasingly required to manage their entire products' life cycles to avoid costly failure or degradation in performance through service/maintenance, more robust design and control, and so on. These product life cycle management (PLM) issues present us with both major challenges and opportunities. There exist considerable incentives in developing appropriate prognostic and diagnostic methodologies for monitoring, analyzing, interpreting, and controlling such abnormal events in complex systems and processes. People in the process and product industries view this as the next major challenge in control systems research and application. In this paper, we will present an overview of these challenges and the opportunities. Recent progress has promising implications on the use of intelligent systems for product lifecycle management applications in the chemical, petrochemical, pharmaceutical and discrete parts manufacturing industries.

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Section: Process History-based Methodsmentioning

confidence: 99%

Section: Process History-based Methodsmentioning

confidence: 99%

Section: A Comparison Of Various Approachesmentioning

confidence: 99%

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Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities

Venkatasubramanian

2005

Computers & Chemical Engineering

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“…Vedam et al [18] used a multiscale representation with coarse and fine resolution linear B-splines which comprise two piecewise linear segments. The multiscale formulation gave spline compression localisation features similar to wavelet compression.…”

Section: Overview Of Data Compressionmentioning

confidence: 99%

“…Therefore the presence of the characteristic linear segments can be detected by counting of zero-valued second differences DðDŷ yÞ calculated from The method can be extended to other piecewise reconstruction methods using polynomials. For instance, if cubic spline compression were in use [18] the fourth derivatives would be zero everywhere except at the knot points where the splines join. In that case the compression factor would be determined from the number of fourth differences having zero values.…”

Section: Compression Detection Proceduresmentioning

confidence: 99%

The impact of compression on data-driven process analyses

Thornhill

Choudhury

Shah

2004

Journal of Process Control

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Stored process data in the form of high fidelity time trends are a resource for data-driven process analyses such as statistical monitoring, minimum variance control loop benchmarking, fault detection, data reconciliation and development of inferential sensors. However, many commercial data historians compress the data before archiving it and a question therefore arises of how useful the compressed data are for the intended purposes.This article examines the impact of compression on data-driven methods and presents an automated algorithm by which the presence of piecewise linear compression may be inferred during the pre-processing phase of a data-driven analysis.The results show that compression interferes with many types of data-driven analyses and the paper strongly recommends caution in the use of compressed process data archives.

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Monitoring of solid oxide fuel cell systems

Murshed

Huang

Nandakumar

2011

Asia-Pacific J Chem Eng

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Fault detection and isolation of critical equipments as well as process operation is an important part of automation. Failure to detect fault can contribute to process safety incident, violation of environmental regulation and, as a result, reduce profit from the unit affected by the fault. Even though there has been a lot of work done on the modeling and control of the solid oxide fuel cell, little attention has been paid to its monitoring methodology. The need of reliable SOFC operations and current effort toward commercialization call for advanced monitoring technology, which constitutes one of the most important directions for SOFC research and development. In this article, as an attempt toward monitoring of SOFC systems a hybrid monitoring approach is developed which formulates the fault detection problem as a linear matrix inequality (LMI). The formulation is then illustrated through its application to the solid oxide fuel cell and its system to handle constraints and detect faults early. 

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A B-spline based method for data compression, process monitoring and diagnosis

Cited by 23 publications

References 6 publications

Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities

Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities

The impact of compression on data-driven process analyses

Monitoring of solid oxide fuel cell systems

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