Knock and Misfire Detection using Ion Current Measurement for Ultra Lean Burn Medium Speed Gas Engine

Yoshimura, Kazuyuki; Tokunaga, Yoshiro; Hashimoto, Dai; Sakurai, Hideaki

doi:10.4271/2007-01-2078

Cited by 11 publications

(9 citation statements)

References 1 publication

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“…The results indicate that it should be possible to use ion current measurement to detect knock and misfire in lean-burn gas engines. 4 Misfire control in the current cycle at engine start has been presented by means of ion sensing technology. The results showed that the misfire phenomenon was identified within 30°crank angle (CA) after the spark plug ignited.…”

Section: Ion Currentmentioning

confidence: 99%

Misfire detection and re-ignition control by ion current signal feedback during cold start in two-stage direct-injection engines

Qiu

Bian

et al. 2012

International Journal of Engine Research

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A misfire detection and re-ignition control device based on an ion current signal is designed, and a method for calculating the threshold level of an ion current integral signal is proposed to judge a misfire event. Based on the combustion cycle control strategy, start-up combustion cycle misfire detection and re-ignition control are investigated in a TSDI-Two Stage Direct Injection gasoline engine using ion current signal feedback. The results show that it is feasible for the ion current integral signal to be used for misfire detection and re-ignition control. In the experiment, when the ion current integral signal is larger than the misfire threshold level (U misfire . 0.5 V), the ion current exists in the combustion chamber, the incylinder fuel-air mixture combusts, and the re-ignition event cannot occur. When the ion current integral signal is smaller than the misfire threshold level (U misfire \ 0.5 V), the ion current is close to zero, the misfire event occurs, and re-ignition takes place. As far as hydrocarbon emissions are concerned, the level of unburnt hydrocarbons under successful ignition is lowest, the level of unburnt hydrocarbons under successful re-ignition is higher, and the level of unburnt hydrocarbons under unsuccessful ignition and re-ignition is the highest. Hence, the start-up combustion cycle misfire detection and reignition control based ion current signal feedback strategy is favorable for reducing unburnt hydrocarbon emissions.

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Section: Ion Currentmentioning

confidence: 99%

Misfire detection and re-ignition control by ion current signal feedback during cold start in two-stage direct-injection engines

Qiu

Bian

et al. 2012

International Journal of Engine Research

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“…Other models have been proposed for optimizing the controller based on the feedback of the ion current signal [10], which has been used for detecting engine knock [11] and misfire [12,13]. Correlations between ion current signal and the air to fuel (A/F) ratio have been reported [14][15][16].…”

Section: Introductionmentioning

confidence: 98%

“…Several investigations in SI engines have proven the ability of the ion current signal to give information about the in-cylinder combustion process, and models have been developed to predict the ion current signal [8,9]. Other models have been proposed for optimizing the controller based on the feedback of the ion current signal [10], which has been used for detecting engine knock [11] and misfire [12,13]. Correlations between ion current signal and the air to fuel (A/F) ratio have been reported [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Effect of design and operating parameters on the ion current in a single-cylinder diesel engine

Badawy

Rai

Singh

et al. 2011

International Journal of Engine Research

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Ion current has been the subject of extensive research in gasoline engines for in-cylinder combustion sensing and as a feedback signal for closed-loop engine control. The sources of the ion current in gasoline engines have been identified. Such identification is not the case in diesel engines. This paper presents experimental data and analysis of the ion current produced in a single-cylinder diesel engine equipped with an electronically controlled common-rail-injection system using an accessible engine control unit. The experiments cover a wide range of engine speeds, loads, injection pressures, and injection timings. The effect of each operating parameter on the shape of the ion current signal, as well as its amplitude, timing, and phase shift relative to the rate of heat release, are determined.

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“…(iv) By using ion current. [13][14][15] This method is ideal because it provides individual cylinder information. Since the ion current is weak, the sensitivity of the ion current sampling device is considered for enhancing detecting precision.…”

Section: Introductionmentioning

confidence: 99%

Misfire detection of diesel engine based on convolutional neural networks

Zhang

Gao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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With the ever-stringent vehicles exhaust emission standard and higher requirements on on-board diagnostic technology, the importance of misfire detection in vehicle emission control is emerging. The performance of a traditional misfire detection algorithm predominantly depends on the features and classifier selected. Fixed and handcrafted features require either a reliable dynamic model of an engine or a large number of experiment data to define the threshold, and then, form a map. Since convolutional neural networks (CNNs) have an inherent adaptive design and integrate the feature extraction with classification functions into a compact learning framework, the misfire fault-sensitive features can be auto-discovered from raw speed signals. Furthermore, CNNs can detect the fault features of the misfire through network training with fewer engine operating conditions. In this paper, the theory and method of the misfire diagnosis based on CNNs are presented. The experimental data for network training and testing are sampled on a six-cylinder inline diesel engine. The misfire patterns containing every one-cylinder and two-cylinder misfire are tested under the wide speed and load conditions of the engine. The results show that when the engine operates under steady-state conditions, one-cylinder or two-cylinder complete misfires can be detected accurately by CNNs. In addition, one-cylinder partial misfire is employed to examine the adaptability of trained 1-D CNN. It turns out that when the partial misfire reaches the same level as half amount of the normal fuel injection quantity, one-cylinder partial misfire can be detected with accuracy more than 96%. At last, the misfire detection under the non-stationary conditions, such as acceleration or deceleration, is conducted. The results show the 1-D CNN performed well in a limited acceleration range, and network failure occurs when the absolute acceleration of the engine speed is more than 100 r/min/s.

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Knock and Misfire Detection using Ion Current Measurement for Ultra Lean Burn Medium Speed Gas Engine

Cited by 11 publications

References 1 publication

Misfire detection and re-ignition control by ion current signal feedback during cold start in two-stage direct-injection engines

Misfire detection and re-ignition control by ion current signal feedback during cold start in two-stage direct-injection engines

Effect of design and operating parameters on the ion current in a single-cylinder diesel engine

Misfire detection of diesel engine based on convolutional neural networks

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