Fault detection using online selected data and updated regression models

Schadler, Doris; Stadlober, Ernst

doi:10.1016/j.measurement.2019.04.010

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…One way to obtain such models is to use regression analysis (Schadler and Stadlober, 2019). All k observations of the response variable are combined into the response vector y = (y 1 , .…”

Section: Data-driven Modelsmentioning

confidence: 99%

A modular adaptive residual generator for a diagnostic system that detects sensor faults on engine test beds

Wohlthan¹,

Pirker²,

Wimmer

2022

J. Sens. Sens. Syst.

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Abstract. It is a great challenge to apply a diagnostic system for sensor fault detection to engine test beds. The main problem is that such test beds involve frequent configuration changes or a change in the entire test engine. Therefore, the diagnostic system must be highly adaptable to different types of test engines. This paper presents a diagnostic method consisting of the following steps: residual generation, fault detection and fault isolation. As adaptability can be achieved with residual generation, the focus is on this step. The modular toolbox-based approach combines physics-based and data-driven modeling concepts and, thus, enables highly flexible application to various types of engine test beds. Adaptability and fault detection quality are validated using measurement data from a single-cylinder research engine and a multicylinder diesel engine.

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“…One way to obtain such models is to use regression analysis (Schadler and Stadlober, 2019). All k observations of the response variable are combined into the response vector y = (y 1 , .…”

Section: Data-driven Modelsmentioning

confidence: 99%

A modular adaptive residual generator for a diagnostic system that detects sensor faults on engine test beds

Wohlthan¹,

Pirker²,

Wimmer

2022

J. Sens. Sens. Syst.

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A multi-stage geometric approach for sensor fault isolation on engine test beds

Wohlthan¹,

Schadler²,

Pirker³

et al. 2021

Measurement

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Optimization of Data Quality Objective on Ammonia Nitrogen System in Water through ASTM Top-Down Approach for Independence Identical Distribution

Wang,

Sun,

Zhang

et al. 2024

Journal of Testing and Evaluation

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Ammonia nitrogen (NH3-N) coming from the instantaneous effluent in urban water is something that is necessary to measure through routine testing. In order to reduce the long-term potentially harmful impacts to ecosystems and human health, we monitor the NH3-N system through an ASTM top-down approach. As the background effect treatments have been debated, the actual extraction efficiency (naturally existing matrix matched) may not be measured for unstable samples greatly fluctuated in wastewater. In this article, we do not spike the matrixes at multiple concentrations; instead, we directly use the parallel samples, within each level consistent differing from each other by up to 5 %. To determine whether the residual or pretreated results derived from the NH3-N system are under the independence identical distribution, we recommend using the more robust Anderson Darling (AD) hypothesis test for the confirmation. If the AD null hypothesis is held, we can believe that the reliability of NH3-N system is validated, and, under the site precision (sR′), incorporate all cumulative effects, even with the interaction and matrix, into the data quality objective (DQO) of the NH3-N system. Our DQO, with variations across all matrices, can be compared to the results obtained historically or subsequently, or with ASTM performance. Our final goal is to meet clients’ needs in a way that allows for operation of the NH3-N system that is consistent, impartial, and competent.

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Fault detection using online selected data and updated regression models

Cited by 3 publications

References 36 publications

A modular adaptive residual generator for a diagnostic system that detects sensor faults on engine test beds

A modular adaptive residual generator for a diagnostic system that detects sensor faults on engine test beds

A multi-stage geometric approach for sensor fault isolation on engine test beds

Optimization of Data Quality Objective on Ammonia Nitrogen System in Water through ASTM Top-Down Approach for Independence Identical Distribution

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