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

Wohlthan, Michael; Schadler, Doris; Pirker, Gerhard; Wimmer, Andreas

doi:10.1016/j.measurement.2020.108313

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…Engine test beds are well explained and a fault detection approach using historical process data is shown in [4]. [5] describes the specific physical relationships applied for fault detection at engine test beds in detail and also explains the important step of fault isolation.…”

Section: Related Results In the Literaturementioning

confidence: 99%

Adaptive Methods for Fault Detection on Research Engine Test Beds

Schadler¹,

Wohlthan²,

Wimmer³

2023

Proceedings of the 18th IMEKO TC10 Conference on Measurement for Diagnostics, Optimisation and Control

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In the case of test beds for research engines, fault detection methods that use models based on historical data face a particular challenge. Due to the experimental design of the test bed, offline training of statistical models with a data set containing all possible variations is simply not possible. The methods must adapt to the current data situation directly on-site. But this involves risks. First, computational time and memory requirements can become extremely large with high data volumes. Second, the data may be faulty and thus negatively affecting the models. To avoid both, a selection of data is made before it is used to build the fault-free reference model. For this purpose, a new statistic is presented as the combination of the Mahalanobis distance and the forecast residual. With it, it is possible to reduce the update frequency and to increase the rate of detected faulty points, since the models are no longer manipulated by faulty data points and thus the residuals provide a better structure for fault detection.

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Section: Related Results In the Literaturementioning

confidence: 99%

Adaptive Methods for Fault Detection on Research Engine Test Beds

Schadler¹,

Wohlthan²,

Wimmer³

2023

Proceedings of the 18th IMEKO TC10 Conference on Measurement for Diagnostics, Optimisation and Control

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“…( 4) and ( 5) are not directly suitable as fault conditions. In practice, it is necessary to introduce a threshold value t that takes these factors into account (Wohlthan et al, 2020), yielding the fault condition…”

Section: Fault Detectionmentioning

confidence: 99%

“…Fault probabilities are used to identify faulty sensors, whereby the following applies: the higher the fault probability, the more likely it is that the values coming from this particular sensor are faulty and, thus, serve (the user) as important information for troubleshooting. A geometrical classification method based on the distance evaluation between error propagation curves and residual state points (Wohlthan et al, 2020) is used for this purpose. As the focus of this paper is on the residual generation and fault detection steps, the fault isolation method will no longer be discussed in this paper.…”

Section: Fault Isolationmentioning

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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“…In the decision-making process though, the authors choose an arbitrary threshold to perform fault detection, which may lead to missed detection of sensor faults or false alarms if it is not well selected. In [16], a model-based sensor fault diagnosis method is proposed for engine test beds using a multistage geometric analysis of the extracted residuals. However, this method considers static models and single-sensor fault occurrence.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Cyber-Physical Framework for Sensor Fault Diagnosis of Marine Internal Combustion Engines

Kougiatsos,

Reppa

2024

IEEE Trans. Contr. Syst. Technol.

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This article proposes a distributed model-based methodology for the diagnosis of faults affecting multiple sensors used for condition monitoring and control of marine internal combustion engines (ICEs). To handle the complexity of the ICE, we consider it as a set of interconnected physical subsystems that constitute the physical layer. For every subsystem, the detection of sensor faults relies on the design of cyber agents, where every agent monitors one subsystem. To handle the heterogeneous dynamics of each subsystem in the fault detection decisionmaking process, each agent uses differential and algebraic residuals alongside adaptive bounds. For isolation purposes, a combinatorial decision logic is employed, realized in two cyber levels: the local and the global decision logic. The first aims at the recognition of all sensor fault patterns that might have affected the engine based on the local agent fault signatures and certain binary decision matrices. The latter is used to capture the propagation of sensor faults between the different monitoring agents. Simulation results are used to showcase the proposed methodology's efficiency in tackling the problem and its applicability.

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

Cited by 9 publications

References 25 publications

Adaptive Methods for Fault Detection on Research Engine Test Beds

Adaptive Methods for Fault Detection on Research Engine Test Beds

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

A Distributed Cyber-Physical Framework for Sensor Fault Diagnosis of Marine Internal Combustion Engines

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