Model Based IAS Analysis for Fault Detection and Diagnosis of IC Engine Powertrains

Abstract

Internal combustion (IC) engine based powertrains are one of the most commonly used transmission systems in various industries such as train, ship and power generation industries. The powertrains, acting as the cores of machinery, dominate the performance of the systems; however, the powertrain systems are inevitably degraded in service. Consequently, it is essential to monitor the health of the powertrains, which can secure the high efficiency and pronounced reliability of the machines. Conventional vibration based monitoring approaches often require a considerable number of transducers due to large layout of the systems, which results in a cost-intensive, difficultly-deployed and not-robust monitoring scheme. This study aims to develop an efficient and cost-effective approach for monitoring large engine powertrains. Our model based investigation showed that a single measurement at the position of coupling is optimal for monitoring deployment. By using the instantaneous angular speed (IAS) obtained at the coupling, a novel fault indicator and polar representation showed the effective and efficient fault diagnosis for the misfire faults in different cylinders under wide working conditions of engines; we also verified that by experimental studies. Based on the simulation and experimental investigation, it can be seen that single IAS channel is effective and efficient at monitoring the misfire faults in large powertrain systems.

Event-Based Angular Speed Measurement and Movement Monitoring

“…Rotating machines are present in many industry areas, like machining, turbines, motors, gears, and shafts, and the rotation speed must be under control to prevent unexpected mechanical failures [ 1 ]. Measurement system for instantaneous rotational speed (IRS) can characterize faults in mechanical systems [ 2 ], such as gears [ 3 , 4 , 5 ], engines [ 6 , 7 , 8 ], shaft torsional vibrations [ 9 ], bearings [ 10 , 11 , 12 ], high rotating speed rotating propellers [ 1 ], estimate life cycle of machines, and evaluate their work conditions. Therefore, a reliable measurement system is required to control these parts’ life cycles.…”

Research on Feature Extraction Method of Engine Misfire Fault Based on Signal Sparse Decomposition

“…Because of its simple structure, output power stability, and convenient installation, the engine is widely utilized as the power output device in many mechanical systems, such as automotive, steamship, and spacecraft [1]. However, after the long-term harsh operation condition, the engine and other transmission system are prone to fault which will cause a sudden power interruption, thus affecting the reliability of the whole transmission system [2][3][4].…”

A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

“…Studies on fault or anomaly detection for vehicle powertrains have been carried out by various approaches. They can be classified by rule-based methods [6][7][8][9][10][11][12], mathematical 2 of 21 model-based methods [13][14][15][16][17][18][19][20][21][22][23][24][25][26], and data-driven methods that use signal processing or machine learning [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43].…”

“…Meyer proposed an inter-turn short circuit fault detection and fault degree identification method using moving horizon observer for the Toyota Prius traction motor [19]. In the case of using data-driven techniques, methods using frequency analysis [27,31,34,40], methods using frequency analysis and neural networks together [41], and methods using machine learning such as one-class SVM, Hidden Markov model, and Gaussian mixture model [28,33,36,42], etc., were proposed. Akin proposed a frequency analysis-based fault detection method used at the motor's zero speed [27].…”

“…Moosavian analyzed the effects of piston scratches on engine vibration using short-time Fourier transform (STFT) and continuous wavelet transform (CWT) [34]. Xu detected engine misfire faults by analyzing the instantaneous angular speed of the engine in the frequency domain [40]. Ewert proposed a fault detection method for motor bearing faults using a machine learning technique based on multi-layer perceptron (MLP), radial basis function (RBF), and selforganizing map (SOM) networks [41].…”