Mining Machine Crowd Failure Prediction for Remote Maintenance
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Solution Overview
Problem
Heavy duty industrial machinery maintenance systems fail to provide accessible information for remote maintenance staff, making it difficult to identify and troubleshoot issues, leading to increased downtime and costs.
Innovation Solution
A system for remotely monitoring heavy duty machinery that collects and analyzes machine data, predicts faults, and provides real-time information to remote maintenance staff, enabling efficient diagnosis and prevention of failures, thereby reducing downtime and maintenance costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If machine data is stored onboard the machine, then data storage is achieved, but remote accessibility for maintenance staff is lost
Solution Approach 1:
The patent introduces a communication module as an intermediary between the machine and remote maintenance staff. This module transmits machine data over a network, enabling remote accessibility without requiring complex direct connections or physical presence at the machine location.
Solution Approach 2:
The patent replaces physical data access mechanisms (onboard storage requiring physical presence) with electronic communication systems. Data is transmitted digitally over networks, eliminating the need for maintenance staff to physically access the machine for data retrieval.
2Reliability
If traditional maintenance schedules are followed, then maintenance is performed regularly, but unplanned downtime occurs due to unpredictable failures
Solution Approach 1:
The patent implements predictive maintenance by monitoring machine data and analyzing trends to predict potential failures before they occur. This preliminary detection allows maintenance to be scheduled in advance, preventing unplanned downtime and ensuring machine availability without following rigid traditional schedules.
Solution Approach 2:
The system continuously collects machine data, analyzes it for anomalies or degradation trends, and provides feedback to predict future failures. This closed-loop feedback mechanism enables dynamic adjustment of maintenance timing based on actual machine condition rather than fixed schedules.
3Ease of repair
If maintenance staff travel to machine locations for diagnosis, then on-site troubleshooting is achieved, but time and costs increase
Solution Approach 1:
The patent creates a virtual copy of the machine's operational state by transmitting data from sensors and systems to remote maintenance staff. This digital copy allows staff to diagnose issues remotely without physically traveling to the machine location, maintaining full diagnostic capability while eliminating travel time.
Solution Approach 2:
The communication network acts as an intermediary that bridges the gap between remote maintenance staff and the physical machine. Data transmitted through this intermediary enables remote diagnosis, replacing the need for physical presence while maintaining comprehensive troubleshooting capability.
Data Source
AI summary
Methods for monitoring a machine are described. In one aspect, a method includes receiving information on a plurality of events associated with the machine, determining a severity value for at least one event of the plurality of events, the severity value based on at least one of a safety value, a hierarchy value, a time-to-repair value, and a cost-of-repair value, and outputting an alert includes the severity value if the severity value exceeds a predetermined threshold associated with the at least one event. Systems and machine-readable media are also described.