Application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine: A case study

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 shown that measured signals taken from the experimental test-bed often contain redundant information and noise due to the nature of the process. In order to deliver a clear interpretation of these measured signals, they therefore need to undergo a ‘compression’ and an ‘extraction’ stage in the modelling process. It is at this stage that the proposed data-driven monitoring technique plays a significant role by taking only the important information of the original measured signals for fault diagnosis purposes. The status of the engine's performance is then monitored using this diagnostic model. This condition monitoring process involves two separate stages of fault detection and root-cause diagnosis.The effectiveness of this diagnostic model was validated using an experimental automotive 1.9 L four-cylinder diesel engine embedded in a chassis dynamometer in an engine test-bed. Two joint diagnostics plots were used to provide an accurate and sensitive fault detection process. Using the proposed model, small air leaks in the inlet manifold plenum chamber with a diameter size of 2–6 mm were accurately detected. Further analyses using contribution to T2 and Q statistics show the effect of these air leaks on fuel consumption. It was later discovered that these air leaks may contribute to emissions fault.In comparison to the existing model-based approaches, the proposed method has several benefits: (i) it makes no simplifying assumptions, as the model is built entirely from the measured signals; (ii) it is simple and straight-forward; (iii) there is no additional hardware required for modelling; (iv) it is a time and cost-efficient way to deliver condition monitoring (i.e. fault diagnosis application); (v) it is capable of pin-pointing the root-cause and the effect of the problem; and (vi) it is feasible to be implemented in practice.