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Engine is one of the major sources of noise in off-highway vehicles. Muffler plays an important role in attenuating the noise emitted by engines. Mufflers are designed to provide maximum noise attenuation without significantly affecting the back pressure. Inlet and outlet arrangements of muffler are proposed based on engine exhaust system and engine compartment design. Multi-inlet and/or multi-outlet mufflers are used based on the needs. This paper details the procedure used for evaluation of transmission loss of a muffler on off-highway vehicle which has two-radial inlets and one axial outlet. 3D simulation is used to obtain the transmission loss parameters. To validate the models, virtual analysis is carried out in two steps. In the first step, one inlet is open to source while the other is blocked and termination at the outlet is anechoic. In the second step, the open and closed inlets are swapped.

To assess the contribution of structure-borne noise from an engine, it is critical to characterize the dynamic and vibro-acoustic properties of the engine components and assembly. In this paper, a component level study of a three-cylinder diesel engine block is presented. Virtual analysis was done to predict the natural frequencies and mode shapes of an engine block in the first step. Then, these results were used to decide the optimum test locations and an experimental modal test was conducted on the engine block. The initial virtual model results for the natural frequencies and mode shapes were correlated with the results from test. Then, the virtual model was updated with the damping derived from experimental modal test to match the vibration frequency response functions. Further, the virtual model was used for prediction of vibro-acoustic transfer functions. The vibro-acoustic transfer functions were also obtained from test.

Early fault detection is vital in maintaining system stability and to decrease the cost associated with maintenance. This paper presents an approach to identify the fuel line failure for a diesel engine based on vibration signals and machine learning. Vibration measurements are performed on the fuel line of the engine for both normal and faulty conditions for engine ramp up condition. After acquiring the time domain vibration signals, various features were extracted and have been analyzed in time and time-frequency domains. Based on the most effective feature, a machine learning model (i.e., support vector machine (SVM)) for fault diagnosis is developed. Results showed that the proposed SVM based model can detect the fuel line fault correctly. This study can be useful for early detection of this critical fault in diesel engine and take useful decision before any catastrophic failure happens because of this fault.