Automotive engine FDI by application of an automated model-based and data-driven design methodology

Svärd, Carl; Nyberg, Mattias; Frisk, Erik; Krysander, Mattias

doi:10.1016/j.conengprac.2012.12.006

Cited by 47 publications

(26 citation statements)

References 37 publications

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“…For more details, please refer to [34]. This model contains 39 equations to describe the dynamic behaviors of the engine (see [33,Appendix]). Among the 39 equations, five are differential equations and the remaining are algebraic equations.…”

Section: B Truck Diesel Engine Systemmentioning

confidence: 99%

Sensor Placement for Fault Isolability Using Low Complexity Dynamic Programming

Chi

Wang

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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In this paper, a novel approach of sensor placement is proposed for the purpose of maximizing fault detectability and isolability. This new approach rests on the basic fact that faults are embedded in the analytical redundancy relations (ARRs) and that the occurrence of a fault will change the consistency of the corresponding ARRs. Based on these basic facts, the minimal isolating (MI) set is introduced to formulate the full/maximal isolability which is the constraint for sensor placement. Consequently, the optimization problem for sensor placement is reformulated as searching an MI set which is related to the least number of candidate sensors. To find the optimal MI set, a low complexity dynamic programming (LCDP) algorithm is developed on the fault set that consists of system faults and sensor faults. However, sensor faults are varied as different candidate sensors are used. Therefore, another dedicated procedure is proposed to handle this issue. A case study shows that the proposed approach outperforms an existing sensor placement approach in terms of efficiency. Note to Practitioners-This paper is motivated by the problem of placing the minimum number of sensors for achieving max-imal isolability in a system. Some existing approaches to search the optimal set of candidate sensors generally have performed relatively low efficiency when a system has many more candidate sensors and system faults. This paper suggests a new approach integrated with dynamic programming (DP). DP inherently qualifies an out-performance on searching solutions from large data. In this research, the approach is further improved by reducing the computational and space complexities of DP, resulting in higher efficiency. The case study results demonstrate that this approach has an out-performance in terms of efficiency. In future research, the sensor placement problem takes more requirements such as sensor weight, sensor volume, and sensor reliability, under consideration besides detectability and isolability.Index Terms-Analytical redundancy relations, dynamic programming, fault detectability and isolability, sensor placement.

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Section: B Truck Diesel Engine Systemmentioning

confidence: 99%

Sensor Placement for Fault Isolability Using Low Complexity Dynamic Programming

Chi

Wang

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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“…Of these, only Nyberg [1], [2] and Nyberg et al [29] deal with model-based schemes. The others use data-driven techniques, either in simulation [30], [32] or from experimental data [31] and although they generally achieve high success rates, it must be remembered that fault data in general, is scarce or not available.…”

Section: Setting Diagnosis Frameworkmentioning

confidence: 99%

MVEM-Based Fault Diagnosis of Automotive Engines Using Dempster–Shafer Theory and Multiple Hypotheses Testing

Vasu

Deb

Mukhopadhyay

2015

IEEE Trans. Syst. Man Cybern, Syst.

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Internal combustion engines exhibit fast pulsating short-time dynamics due to the reciprocating cylinder motion, around mean operating points that change comparatively slow due to inputs such as throttle and load. Comparatively, simple mean value engine models (MVEM) describe the slow changes of the averaged states for automotive control and fault diagnosis. In this paper, a bank of state estimators based on MVEMs is used for fault residual generation. Three faults: 1) throttle mass airflow sensor fault; 2) exhaust gas recirculation valve sensor fault; and 3) exhaust leak fault are considered here. These faults are significant as they affect emission levels. Optimized thresholds for residual classification are derived for minimizing false alarm rates and missed detection rates. The diagnosis logic, based on the principles of structured residuals proposed in literature, is extended here for multiple hypotheses testing. Furthermore, the Dempster-Shafer theory is used to associate a confidence measure with the decision conclusions and this is shown to improve isolation. Performance is demonstrated with automotive engine data obtained from a four-cylinder instantaneous spark-ignition engine (gasoline) system model, developed in the simulation software AMESim.

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“…One is that the injected faults may trigger the on-board diagnosis system to shutoff the system or compensate for the fault. The other one is that the injected faults may lead to potential damage of the system or can be fatal for the driver, if we test the FDI system on the road [27]. Therefore, the offline strategy is chosen to cover the considered faults in a consistent way as well as to illustrate the proposed FDI scheme in this paper.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Design and Evaluation of Model-Based Health Monitoring Scheme for Automated Manual Transmission

Chen

Ahmed

Rizzoni

et al. 2016

Journal of Dynamic Systems, Measurement, and Control

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Health monitoring of automated manual transmission (AMT) in modern vehicles can play a critical role to avoid its malfunctions and ensure vehicle functional safety. In order to meet this demand, this paper presents a model-based fault detection and identification (FDI) scheme for AMT. After developing the fault model of AMT, structural analysis (SA)-based fault detectability and isolability is realized with the available set of sensors, prior to design and development of residuals. The residuals are generated by employing the theory of SA, where the concepts of analytical redundant relationship (ARR) are utilized to make residuals stable and robust. Finally, the proposed FDI scheme is successfully evaluated to detect and isolate the sensor faults in EcoCAR2 AMT.

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Automotive engine FDI by application of an automated model-based and data-driven design methodology

Cited by 47 publications

References 37 publications

Sensor Placement for Fault Isolability Using Low Complexity Dynamic Programming

Sensor Placement for Fault Isolability Using Low Complexity Dynamic Programming

MVEM-Based Fault Diagnosis of Automotive Engines Using Dempster–Shafer Theory and Multiple Hypotheses Testing

Design and Evaluation of Model-Based Health Monitoring Scheme for Automated Manual Transmission

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