Multi Sensor-Based Failure Diagnosis using the Mahalanobis Taguchi System

Abstract

Abstract Heavy equipment in the mining industry is an essential instrument in achieving the company’s production targets. But on the other hand, heavy equipment is complicated and expensive equipment. These problems can be overcome by leasing the equipment to an external agent. In the lease contract between the lessee and lessor, it is discussed about the maintenance strategy of the equipment that will be rented by the lessee. This research presents a real-time monitoring scheme based on multi-sensor data installed on the machine. Case studies from this research were carried out on excavator units in heavy equipment rental companies. The Mahalanobis Taguchi System (MTS) method is used to handle multi-sensor data. Multi-sensor data is grouped into normal condition groups and abnormal condition groups. The variables used to monitor excavator conditions include vibration, pressure and temperature sensors. Based on the results of the calculation of the excavator threshold conditions using the Mahalanobis Distance (MD) measurement technique, the threshold of normal conditions is (-2.137 to 4.121). In contrast, in abnormal accumulator conditions (4, 331 to 39, 458), pump failure (40, 956 to 138, 048), valve failure (2708, 104 to 3404, 187) and the threshold in the condition of the cooler failure is (10736.160 to 11434.151). This research shows that the MD-based CBM scheme produced can detect, identify, and isolate the failure of excavator components under study.