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To improve overall customer experience, it is imperative to minimize the noise levels inside agricultural equipment cab. Up-front prediction of acoustic performance in product development is critical to implement the noise control strategies optimally. This paper discusses the methodology used for virtual modeling of a cab on agricultural equipment for prediction of interior noise. The Statistical Energy Analysis (SEA) approach is suitable to predict high frequency interior noise and sound quality parameters such as articulation index and loudness. The cab SEA model is developed using a commercial software. The structural and acoustic excitations are measured through physical testing in various operating conditions. The interior noise levels predicted by the virtual model are compared with the operator ear noise levels measured in the test unit. The resultant SPL spectrum from SEA correlates well with the test.

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.

Whole Body Vibration (WBV) of tractors was measured on different surfaces in real world usage pattern of Indian customers on tractors of various capacities. Vibration levels were measured at the interface of the seat and the operator, on the seat base/floor and on the head. The mean weighted Root Mean Square (RMS) values along the different axes, the vector sum of weighted RMS values along the three orthogonal axes, the crest factor, Vibration Dose Value (VDV) and 8 h exposure levels were calculated according to ISO 2631-1. In addition to the above parameters, the transmissibility between the seat base and the seat interface (SEAT) and between the seat interface and the operator head (TR) were also calculated. Finally, these parameters were correlated with the subjective feel of customers which was captured through suitable questionnaires. It is observed that the Indian tractor operators are exposed to WBV that exceeds the cautionary boundaries set in place by the ISO 2631-1.

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.