Real-time abnormality detection and classification in diesel engine operations with convolutional neural network

Shahid, Syed Maaz; Ko, Sung H.; Kwon, Sungoh

doi:10.1016/j.eswa.2021.116233

Cited by 40 publications

(10 citation statements)

References 33 publications

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“…The results of the diagnostic accuracy are shown in Table 6 . The proposed method is compared with sequential minimum optimization for support vector machines (SMO-SVM), Multilayer Perceptron (MLP) [ 31 ], one-dimensional convolutional neural networks (1DCNN) [ 29 ], long and short term memory recurrent neural networks (LSTM-RNN) [ 30 ], and residual neural networks (ResNet) [ 31 ]. The 1DCNN and LSTM-RNN ran for 200 epochs, while ResNet ran for 30 epochs.…”

Section: Vmd-rf Fault Detection Methodsmentioning

confidence: 99%

“…Supervised pattern classification methods such as deep neural networks (DNN) are more suitable due to their powerful learning capabilities. Shahid et al used a one-dimensional convolutional neural network (1DCNN) to identify the crankshaft angle degree of the engine and successfully diagnosed the misfire fault [ 29 ]. Zhang et al proposed a long short-term memory recurrent neural network (LSTM-RNN) for evaluating bearing degradation and proposed waveform entropy to improve the accuracy effectively [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

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Single-Sensor Engine Multi-Type Fault Detection

Tang

Cheng

et al. 2023

Sensors

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Engine fault detection is conducive to improving equipment reliability and reducing maintenance costs. In practical scenarios, high-quality data is difficult to obtain. Usually, only single-sensor data is available. This paper proposes a fault detection method combining Variational Mode Decomposition (VMD) and Random Forest (RF). At first, the spectral energy distribution is obtained by decomposing and statistic the engine data of multiple working conditions. Based on the spectral energy distribution, the overall optimal mode number was identified, and the quadratic penalty term was optimized using SNR. The improved VMD (IVMD) improves mode aliasing and iterative efficiency and unifies feature dimensions. Decomposition of real signals demonstrates the effectiveness. The paper designs a feature vector composed of seven types of attributes, including unit bandwidth energy, center frequency, maximum singular value and so on. The feature vector is then fed to RF for classification. Features are selected in order of importance to classification to improve the training efficiency. By comparing with various algorithms, the proposed method has higher accuracy and faster training efficiency in single-speed, multi-speed and cross-speed single-sensor data diagnosis. The results show that the method has application prospects with little training data and low hardware requirements.

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Section: Vmd-rf Fault Detection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Single-Sensor Engine Multi-Type Fault Detection

Tang

Cheng

et al. 2023

Sensors

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“…Techniques Metrics [34] CAD signal A five layers 1D CNN Accuracy (%) 99.7 [26] Time-domain statistical features (rectified mean value, mean value, peak value, RMS, KF, P, CF, shape factor, and margin factor) and wavelet packet energy features…”

Section: Source Featuresmentioning

confidence: 99%

Torque Regression Using Machine Learning Techniques in Automotive ECUs

Canal,

Bonomo,

de Carvalho

et al. 2024

Preprint

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With the modernization of the automotive industry, a large amount of data is generated by vehicles during operation. When interpreted analytically, it can result in advances in several areas, such as safety, durability, production efficiency, fuel consumption, and gas emissions. In this work, we focus on engine torque and its regression, to enable the understanding of its behavior and help improve the performance of engines and vehicles, mainly in product development and testing. In addition, it highlights the relationship between torque and misfire for optimizing engine components by showing their impacts, as it is a common failure in combustion engines that impairs engine performance. After a rigorous process of feature selection relevant to torque, we collect data directly from a vehicle's ECU to train and evaluate machine learning algorithms to perform torque regression without relying on synthetic data or public datasets, using misfire detection as one of the inputs to the algorithms. In a car that reaches a torque of 144 Nm, our results reached up to an RMSE of 3.3830 (Nm)², MAE of 2.1620 (Nm), and R² of 0.99. Furthermore, our methodology acquired and processed data in real-time at a cloud server, that detects faults and calculates torque with the vehicle in motion, using less computationally demanding algorithms. These findings not only highlight our ability to predict the engine's torque and its relation with misfires but also our competence in analyzing additional parameters essential to vehicle performance.

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“…Li et al [26] proposed an anomaly detection CNN model for substations and demonstrated its superior performance to ANN, KNN, and RF models. Shahid et al [27] used a CNN model to detect engine anomalies and compared it with SVM-, KNN-, and CNN-based models. Lee et al [28] transformed time-series data into twodimensional (2D) images, proposed an anomaly detection model for nuclear power plants using two-channel CNN [29], and compared its performance with that of one-channel CNN, the gated recurrent unit (GRU) [30], ANN, and SVM.…”

Section: Related Workmentioning

confidence: 99%

CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention

Chung,

Gu,

Yoo

2023

Sensors

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The extreme operating environment of the combined heat and power (CHP) engine is likely to cause anomalies and defects, which can lead to engine failure; thus, detecting engine anomalies is essential. In this study, we propose a parallel convolutional neural network–long short-term memory (CNN-LSTM) residual blocks attention (PCLRA) anomaly detection model with engine sensor data. To our knowledge, this is the first time that parallel CNN-LSTM-based networks have been used in the field of CHP engine anomaly detection. In PCLRA, spatiotemporal features are extracted via CNN-LSTM in parallel and the information loss is compensated using the residual blocks and attention mechanism. The performance of PCLRA is compared with various hybrid models for 15 cases. First, the performances of serial and parallel models are compared. In addition, we evaluated the contributions of the residual blocks and attention mechanism to the performance of the CNN–LSTM hybrid model. The results indicate that PCLRA achieves the best performance, with a macro f1 score (mean ± standard deviation) of 0.951 ± 0.033, an anomaly f1 score of 0.903 ± 0.064, and an accuracy of 0.999 ± 0.002. We expect that the energy efficiency and safety of CHP engines can be improved by applying the PCLRA anomaly detection model.

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Real-time abnormality detection and classification in diesel engine operations with convolutional neural network

Cited by 40 publications

References 33 publications

Single-Sensor Engine Multi-Type Fault Detection

Single-Sensor Engine Multi-Type Fault Detection

Torque Regression Using Machine Learning Techniques in Automotive ECUs

CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention

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