Predictive Maintenance Solution with Real-Time Insights and Cost Estimation for Machine Health Management

This paper introduces a comprehensive solution for predictive maintenance, utilizing statistical data and analytics. The proposed Service Planner feature offers customers real-time insights into the health of machine or vehicle parts and their replacement schedules. By referencing data from service stations and manufacturer advisories, the Service Planner assesses the current health and estimated lifespan of parts based on metrics such as days, engine hours, kilometers, and statistical data. The user interface displays current part health, replacement due dates, and estimated replacement costs. For example, if air filter replacement is recommended every six months, the solution uses manufacturer advisories to estimate the remaining life of the air filter in terms of days or engine hours. It also suggests replacement dates, suitable part options, replacement costs, and available service slots through an operator guidance mobile app and portal. The solution features a 360-degree view of the machine or vehicle, providing detailed information on each part and allowing operators to interact with and select parts of interest. An integrated cost estimator offers users estimated service costs and availability at authorized service centers, using a centralized part database. This solution empowers customers to monitor machine health, gain a better understanding of their machines, and receive service advisories to prevent breakdowns and downtime. Additionally, the cost estimation feature aids in better planning and budgeting for maintenance.