An Integrated Diagnostic Development Process for Automotive Engine Control Systems

Luo, Jie; Pattipati, Krishna R.; Liu, Qiao; Chigusa, S.

doi:10.1109/tsmcc.2007.900659

Cited by 39 publications

(10 citation statements)

References 25 publications

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“…We ran our prototype implementation on a small real-world automotive example. The example is based on a mixed discrete-continuous model of an engine air intake test-bed [11]. It has been turned into a coarse CSP model by abstracting continuous system variables into suitable finite domains with up to 12 values, corresponding to different operating regions.…”

Section: Computational Resultsmentioning

confidence: 99%

Using Model Counting to Find Optimal Distinguishing Tests

Stefan

Sachenbacher

2009

Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems

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Testing is the process of stimulating a system with inputs in order to reveal hidden parts of the system state. In the case of nondeterministic systems, the difficulty arises that an input pattern can generate several possible outcomes. Some of these outcomes allow to distinguish between different hypotheses about the system state, while others do not. In this paper, we present a novel approach to find, for non-deterministic systems modeled as constraints over variables, tests that allow to distinguish among the hypotheses as good as possible. The idea is to assess the quality of a test by determining the ratio of distinguishing (good) and not distinguishing (bad) outcomes. This measure refines previous notions proposed in the literature on model-based testing and can be computed using model counting techniques. We propose and analyze a greedy-type algorithm to solve this test optimization problem, using existing model counters as a building block. We give preliminary experimental results of our method, and discuss possible improvements.

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Section: Computational Resultsmentioning

confidence: 99%

Using Model Counting to Find Optimal Distinguishing Tests

Stefan

Sachenbacher

2009

Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems

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“…Although basic research in model-based diagnosis has matured, there is still a lack of sufficient knowledge on how to integrate different diagnostic modelling techniques, especially those that combine mathematical and graph-based dependency models, for an intelligent diagnosis. Luo et al [163] presented a hybrid model-based diagnostic method to improve the telematic diagnostics, the diagnostic system’s accuracy, and the consistency of those solely based on graph-based models. Luo et al [163] developed a fault-diagnosis toolset, comprised of both model-based and data-driven techniques to provide a ‘sand box’ for test engineers to experiment with, and to systematically select relevant algorithms/techniques to detect and isolate their specific fault problems.…”

Section: Emerging Areas Of Research In Fault Diagnosis Of Power Train Systemsmentioning

confidence: 99%

A survey on diagnostic methods for automotive engines

Mohammadpour

Franchek

Grigoriadis

2011

International Journal of Engine Research

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Faults affecting automotive engines can potentially lead to increased emissions, increased fuel consumption, or engine damage. These negative impacts may be prevented or at least alleviated if faults can be detected and isolated in advance of a failure. United States Federal and State regulations dictate that automotive engines operate with high-precision onboard diagnosis (OBD) systems that enable the detection of faults, resulting in higher emissions that exceed standard thresholds. In this paper, we survey and discuss the different aspects of fault detection and diagnosis in automotive engine systems. The paper collects some of the efforts made in academia and industry on fault detection and isolation for a variety of component faults, actuator faults, and sensor faults using various data-driven and model-based methods.

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“…We evaluated our DNNF-based testing method on a model of an automotive engine test-bed [10], which consists of three major components: engine, pipe, and throttle. The goal is to find leaks in the pipe by assigning three to four controllable variables, and observing three to four measurable variables.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Constraint-Based Optimal Testing Using DNNF Graphs

Schumann

Sachenbacher

Huang

2009

Principles and Practice of Constraint Programming - CP 2009

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The goal of testing is to distinguish between a number of hypotheses about a system-for example, different diagnoses of faultsby applying input patterns and verifying or falsifying the hypotheses from the observed outputs. Optimal distinguishing tests (ODTs) are those input patterns that are most likely to distinguish between hypotheses about non-deterministic systems. Finding ODTs is practically important, but it amounts in general to determining a ratio of model counts and is therefore computationally very expensive. In this paper, we present a novel approach to constraint-based ODT generation, which uses structural properties of the system to limit the complexity of computation. We first construct a compact graphical representation of the testing problem via compilation into decomposable negation normal form. Based on this compiled representation, we show how one can evaluate distinguishing tests in linear time, which allows us to efficiently determine an ODT. Experimental results from a real-world application show that our method can compute ODTs for instances that were intractable for previous approaches.

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An Integrated Diagnostic Development Process for Automotive Engine Control Systems

Cited by 39 publications

References 25 publications

Using Model Counting to Find Optimal Distinguishing Tests

Using Model Counting to Find Optimal Distinguishing Tests

A survey on diagnostic methods for automotive engines

Constraint-Based Optimal Testing Using DNNF Graphs

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