Machine Learning Models for Suspension System Performance Prediction in Large Dump Trucks

For achieving bulk economic excavation, large dump trucks are being used in majority of the earth moving operations resulting in high impact shovel loading operations (HISLO) which exposes the operators to high levels of whole body vibrations (WBV). As the truck ages, hydro-pneumatic suspension struts loses their capability to effectively attenuate the vibration. A system is required to monitor and predict the performance of the suspension struts in real time. Machine learning and artificial intelligence (AI) has been applied for modeling and predicting the suspension system performance for lighter/smaller vehicles. However, no work has been done to implement machine learning or AI for modelling and predicting the performance of hydro-pneumatic struts in large dump trucks. Therefore, current work is going to be a pioneering effort towards developing machine learning and AI models for solving this problem. Support vector machine (SVM) and regularized dual non-linear regression has been implemented in order to achieve the goal. 3D virtual simulator for CAT793D was used to conduct experiments in MSC.ADMAS environment. The two main recorded outputs were the RMS accelerations in the vertical and horizontal directions at the operator seat for characterizing the performance of the suspension system. During the development and the training of the models, eighty percent (80%) of the total experimental data was used and the remaining 20% was used during the testing and validation of the developed models. SVM model showed the desired accuracy in terms of hydro-pneumatic suspension system performance prediction for large dump trucks. These models can be implemented in the dump truck controller which can then be used to monitor the performance of the suspension system in real-time, and with that proper maintenance and/or replacement can be scheduled by the maintenance personnel. Workplace safety, operator’s health and the overall system efficiency can be greatly improved with an implementation of such an intelligent system. INTRODUCTION Large capacity dump trucks are being employed in any earth moving operation throughout the world for achieving bulk economic excavation [1, 2]. A large dynamic impact force is produced with large capacity shovels dumping material weighing 100 tons or more in to the dump truck under gravity. This dynamic impact force generates high frequency shockwaves which travels throug0068 the truck body, chassis and reaches the operator’s cabin and thus exposes the operator to severe levels of whole body vibrations (WBV). Recommended safe limits have been listed by the International Standards Organization (ISO) in sections 1, 2, 4 and 5 of ISO 2631 (1997, 2003, 2001 and 2004). An operator can got through a long term back, shoulders, arms and neck disability and disorders if exposed to the WBV levels beyond those limits. The dynamic impact force generated as a result of the high impact shovel loading operations (HISLO) was modelled by Ali and Frimpong [2]. Furthermore, the complete methodology for optimizing the dumping process and reducing the impact force has been provided by Ali and Frimpong [1]. The vibration attenuation system present in the dump truck requires improvement in addition to the dumping process optimization. The vibration attenuation system in the dump truck commonly consists of hydro-pneumatic suspension struts. As the dump truck ages, its suspension system deteriorates and therefore loses its capability to effectively attenuate the shockwaves and the resulting vibrations produced by the dumping process. The deterioration tales place due to the contamination of the hydraulic oil or oil and nitrogen mixing or due to the gas accumulator diaphragm rupturing. The monitoring of the output vibration levels can be used to evaluate the effectiveness of any suspension system. With the dump truck suspension system, the vibration levels at the operator’s seat can be used for the evaluation of the effectiven