Engine Misfire Diagnosis Based on the Torsional Vibration of the Flexible Coupling in a Diesel Generator Set: Simulation and Experiment

Liu, Zhiwei; Wu, Kun; Ding, Qian; Gu, Jiahui

doi:10.1007/s42417-019-00097-1

Cited by 20 publications

(8 citation statements)

References 9 publications

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“…There is a long history of works focused on misfire fault detection in internal combustion engines. Many approaches utilize vibration analysis [ 23 , 24 , 25 , 26 , 27 , 28 , 29 ] and physics-based estimations such as with acceleration, torque, and speed [ 30 , 31 , 32 , 33 ].…”

Section: Prior Artmentioning

confidence: 99%

Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization

Terwilliger

Siegel

2022

Sensors

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In a world dependent on road-based transportation, it is essential to understand automobiles. We propose an acoustic road vehicle characterization system as an integrated approach for using sound captured by mobile devices to enhance transparency and understanding of vehicles and their condition for non-expert users. We develop and implement novel deep learning cascading architectures, which we define as conditional, multi-level networks that process raw audio to extract highly granular insights for vehicle understanding. To showcase the viability of cascading architectures, we build a multi-task convolutional neural network that predicts and cascades vehicle attributes to enhance misfire fault detection. We train and test these models on a synthesized dataset reflecting more than 40 hours of augmented audio. Through cascading fuel type, engine configuration, cylinder count and aspiration type attributes, our cascading CNN achieves 87.0% test set accuracy on misfire fault detection which demonstrates margins of 8.0% and 1.7% over naïve and parallel CNN baselines. We explore experimental studies focused on acoustic features, data augmentation, and data reliability. Finally, we conclude with a discussion of broader implications, future directions, and application areas for this work.

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Section: Prior Artmentioning

confidence: 99%

Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization

Terwilliger

Siegel

2022

Sensors

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“…The modal parameters can be obtained from the inertia and stiffness matrix, and the first five modes of the model are listed in Table 5. These resonant responses are generally considered to be negative for the accurate detection and diagnosis when using the IAS signals [19]. In the simulation studies, the resonant responses in the torsional vibration were investigated to denote the fair diagnostic performance.…”

Section: θ} + C{mentioning

confidence: 99%

“…Mendes et al modelled the crankshaft as a flexible rotor, for which the outputs are similar to the experimental signals [18]. Liu et al developed an nonlinear model of the engine powertrain to compare the performances of IAS and torque measurements when detecting and diagnosing the misfire faults [19]. These research activities show that the model based investigation can be accurate and reliable, and therefore, this paper focuses on using simulation studies to develop the fault detection and diagnosis approach and the optimal measurement position for IAS.…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Yun

et al. 2020

Energies

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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.

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“…El-Mongy and Younes [15] analyzed vibrational transient response of a cracked rotor in the presence of unbalance and coupling misalignment. Liu et al studied the torsional vibration and torque fluctuation of the coupling due to diesel engine misfire [16] and the transient vibration response of angular misaligned rotors with rubbing effect during speed-up process [17].…”

Section: Introductionmentioning

confidence: 99%

Vibration responses of rotor systems in diesel multiple units under dynamic spatial misalignments and base motions

Liu

Ding³

et al. 2021

Journal of Sound and Vibration

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To achieve high performance monitoring of the electric transmission powerpack for a Diesel Multiple Unit (DMU), the dynamic responses of the rotor system inside the powerpack with Dynamic Spatial Misalignment (DSM) and base motions from car body are investigated through structural modeling and numerical analysis. A three-dimensional coupled model, including public framework, diesel engine, electric generator and rotor system, is developed. Vibrations under deterministic and random base motions are numerically calculated using the Newmark Method. The simulation results show that vibration responses of the coupled model are evidently different from those of rotors with a fixed base. DSM is more nonlinearly sensitive to the deterministic base motion than rotor vibrations, and the converse is true when the base pitch motion in is random in frequency. Additionally, results show that DSM may vary significantly in different axial positions, with large base motions in some extreme situations. These findings lay the primary foundations for implementing vibration-based condition monitoring of DMU diesel-generator systems.

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Engine Misfire Diagnosis Based on the Torsional Vibration of the Flexible Coupling in a Diesel Generator Set: Simulation and Experiment

Cited by 20 publications

References 9 publications

Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization

Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization

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

Vibration responses of rotor systems in diesel multiple units under dynamic spatial misalignments and base motions

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