Predictive Maintenance for Industrial IoT of Vehicle Fleets using
  Hierarchical Modified Fuzzy Support Vector Machine
  Connected vehicle fleets are deployed worldwide in several industrial IoT
scenarios. With the gradual increase of machines being controlled and managed
through networked smart devices, the predictive maintenance potential grows
rapidly. Predictive maintenance has the potential of optimizing uptime as well
as performance such that time and labor associated with inspections and
preventive maintenance are reduced. In order to understand the trends of
vehicle faults with respect to important vehicle attributes viz mileage, age,
vehicle type etc this problem is addressed through hierarchical modified fuzzy
support vector machine (HMFSVM). The proposed method is compared with other
commonly used approaches like logistic regression, random forests and support
vector machines. This helps better implementation of telematics data to ensure
preventative management as part of the desired solution. The superiority of the
proposed method is highlighted through several experimental results.
