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This paper presents a case study of the application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine. Using measurement signals taken from the sensors/actuators which are present in a modern automotive vehicle, a data-driven diagnostic model is built for condition monitoring purposes. Detailed investigations have(More)
This paper presents an innovative diagnostic method tailored for automotive electronic system diagnostic tools. By incorporating a Bayesian Belief Network (BBN) technique, the proposed method is capable of guiding vehicle diagnostics in a probabilistic manner. In addition, the method features a multiple-DTC-orientated troubleshooting strategy, and is(More)
  • David Antory
  • 2005
The application of a new method for fault diagnosis in an automotive diesel engine is presented. Two common types of fault are investigated: (i) sensor fault, caused by a bias in the inlet manifold pressure sensor and (ii) process fault, caused by small air leaks in the inlet manifold plenum chamber. Such faults may lead to increased emission levels which,(More)
This paper presents simple and practical methodologies for early engine misfire detection. Two diagnostics models, one based on standard linear system identification approaches and a second using a novel nonlinear extension of the linear approaches, involving a multilayer perceptron neural network, were investigated. The models were validated using(More)
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