A virtual injection sensor by means of time frequency analysis

Ferrari, Alessandro; Paolicelli, Federica

doi:10.1016/j.ymssp.2018.07.009

Cited by 10 publications

(7 citation statements)

References 22 publications

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“…It has been proven that there is a coupling relationship between the injection process and the high pressure of the incoming flow. 26,27 The pressure fluctuation caused by the injection process can be used as a sensing signal to transmit the injection rate. Coppo 28 integrated a pressure sensor into the pressure chamber of an injector.…”

Section: Introductionmentioning

confidence: 99%

“…The concept of an intelligent fuel injector was proposed, quickly providing feedback information to the control unit. Ferrari and Paolicelli 27,29 extracted the time required for fuel injection through a time-frequency analysis of the pressure signal at the injector inlet; an online calculation method for the injection rate was proposed. Marker and Willmann 30 studied the diagnosis of large light fuel oil (LFO) diesel engines, focusing on the diagnosis of the fuel injector.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of fuel injection rate identification algorithm based on rail pressure fluctuation characteristics induced by injection

Lei

Qiu

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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As an important part of the common-rail (CR) fuel system for diesel engines, the injector circulation capacity and the fuel injection mass flow rate vary with carbon deposition and wear, affecting the engine output performance. This study proposes a method to identify the fuel injection rate online, based on the rail pressure fluctuation characteristics induced by fuel injection. The control algorithm uses the signal from the existing rail pressure sensor; the diesel engine does not require modification or additional sensors. A quasi-dimensional model of the CR fuel system was built to analyse the rail pressure wave fluctuation characteristics, and a parameter K was defined as the pressure drop rate. Based on K, a control algorithm was proposed. A high-pressure fuel pump test rig was built to test the fuel injection performance under different operating conditions, and the experimental data were processed by wavelet transform. From the test data, the K of the CR system was analysed using the feedback of the rail pressure sensor. The experimental results show that the value of K increases with an increase in the initial pressure and injection pulse, and is independent of the injection mode. The algorithm is feasible, and works more accurately with a longer injection pulse and a lower pump speed. This method uses the existing rail pressure sensor, does not incur extra cost and has great potential for improving the injection accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of fuel injection rate identification algorithm based on rail pressure fluctuation characteristics induced by injection

Lei

Qiu

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…erefore, due to the time-varying frequency spectrum of transient signals, suitable time-frequency analysis tools are needed for realtime monitoring and fault diagnosis. Time-frequency analysis can identify the signal frequency components and reveal their time-variant features, which has been an effective tool for monitoring and fault diagnosis by extracting feature information contained in nonstationary signals [5,6]. In recent years, researchers have introduced image processing technology into the field of diesel engine fault diagnosis, using signal time spectrum maps for image feature extraction and classification recognition.…”

Section: Introductionmentioning

confidence: 99%

A Novel Improved Local Binary Pattern and Its Application to the Fault Diagnosis of Diesel Engine

Cai

et al. 2020

Shock and Vibration

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Aiming at the feature extraction difficulty of vibration signals, an improved local binary pattern-(ILBP-) based diesel engine fault diagnosis approach is proposed. To effectively make use of the component spatial information in time-frequency images, local binary pattern (LBP) algorithm is applied. Also, in view of the problems that traditional LBP coding is easily interfered by singular pixel points and the relative spatial information is not prominent, an improved coding rule of the LBP operator is put forward in this paper. Compared with some typical LBP algorithms, computational complexity of the proposed ILBP algorithm is greatly reduced, and the coding sparsity is greatly improved. e ILBP operator is applied to fault diagnosis of BF4L1011F diesel engine with eight different valve conditions. For comparison, six kinds of time-frequency distribution are used to convert raw vibration signals into time-frequency images, and then circular LBP, rotation-invariant LBP, uniform LBP, and ILBP operator are applied for texture coding. Finally, nearest neighbor classifier (NNC) and support vector machine (SVM) are used for fault identification.e classification results show that the ILBP operator proposed in this paper can better describe the texture feature information in vibration time-frequency images of the diesel engine, and a good diagnostic effect can be achieved by combining wavelet packet (WP) distribution and ILBP.Hindawi Shock and Vibration Volume 2020, Article ID 9830162, 15 pages https://doi.

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“…As a result, the tracking error of the steady-state speed was only about 4% and required a certain convergence time. Ferrari [13] proposed that pressure of nozzle opening and closing was identified by means of pressure data fitted by bench test. Then the time-frequency analysis was used to obtain the mean instantaneous frequency and adjust the control strategy of the injector.…”

Section: Introductionmentioning

confidence: 99%

“…Observer-based method [12,13,14,15] is proven to be a good way to predict the output of SISO system. Engine cylinder pressure signal can be used to characterize the engine combustion state [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Virtual In-Cylinder Pressure Sensor Based on EKF and Frequency-Amplitude-Modulation Fourier-Series Method

Wang

Sun

et al. 2019

Sensors

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As a crucial and critical factor in monitoring the internal state of an engine, cylinder pressure is mainly used to monitor the burning efficiency, to detect engine faults, and to compute engine dynamics. Although the intrusive type cylinder pressure sensor has been greatly improved, it has been criticized by researchers for high cost, low reliability and short life due to severe working environments. Therefore, aimed at low-cost, real-time, non-invasive, and high-accuracy, this paper presents the cylinder pressure identification method also called a virtual cylinder pressure sensor, involving Frequency-Amplitude Modulated Fourier Series (FAMFS) and Extended-Kalman-Filter-optimized (EKF) engine model. This paper establishes an iterative speed model based on burning theory and Law of energy Conservation. Efficiency coefficient is used to represent operating state of engine from fuel to motion. The iterative speed model associated with the throttle opening value and the crankshaft load. The EKF is used to estimate the optimal output of this iteration model. The optimal output of the speed iteration model is utilized to separately compute the frequency and amplitude of the cylinder pressure cycle-to-cycle. A standard engine’s working cycle, identified by the 24th order Fourier series, is determined. Using frequency and amplitude obtained from the iteration model to modulate the Fourier series yields a complete pressure model. A commercial engine (EA211) provided by the China FAW Group corporate R&D center is used to verify the method. Test results show that this novel method possesses high accuracy and real-time capability, with an error percentage for speed below 9.6% and the cumulative error percentage of cylinder pressure less than 1.8% when A/F Ratio coefficient is setup at 0.85. Error percentage for speed below 1.7% and the cumulative error percentage of cylinder pressure no more than 1.4% when A/F Ratio coefficient is setup at 0.95. Thus, the novel method’s accuracy and feasibility are verified.

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A virtual injection sensor by means of time frequency analysis

Cited by 10 publications

References 22 publications

Investigation of fuel injection rate identification algorithm based on rail pressure fluctuation characteristics induced by injection

Investigation of fuel injection rate identification algorithm based on rail pressure fluctuation characteristics induced by injection

A Novel Improved Local Binary Pattern and Its Application to the Fault Diagnosis of Diesel Engine

A Virtual In-Cylinder Pressure Sensor Based on EKF and Frequency-Amplitude-Modulation Fourier-Series Method

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