Machine Maintenance Prioritization Using Sensor Log Correlation
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Solution Overview
Problem
Existing methods for determining maintenance tasks in machines often fail to accurately identify and rectify anomalies or faults, leading to wasted time and resources due to ineffective maintenance tasks.
Innovation Solution
A method that analyzes historical data from a target machine and comparable machines to determine statistical metrics, rank sensor logs, and correlate maintenance tasks, selecting a priority maintenance task based on these analyses to effectively address anomalies or faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If local sensor monitoring and predefined range checks are used for machine maintenance, then the monitoring can be performed locally with simple processing, but the accuracy of identifying actual maintenance needs deteriorates due to lack of contextual information
Solution Approach 1:
The patent combines local sensor monitoring with remote historical data and contextual information from multiple sources. The system merges real-time local data with past sensor logs, maintenance records, and operational context to create a comprehensive view that improves maintenance need identification accuracy while maintaining local monitoring simplicity.
Solution Approach 2:
The patent introduces an intermediary system that acts as a bridge between local monitoring and centralized analysis. This intermediary collects local sensor data, transmits it to a centralized system for contextual analysis using historical logs and AI algorithms, and returns refined maintenance recommendations, thus preserving local ease of operation while achieving high identification accuracy.
2Measurement precision
If comprehensive historical data from multiple machines is analyzed to improve maintenance accuracy, then the precision of maintenance task determination improves, but the complexity of data processing and system architecture increases
Solution Approach 1:
The patent segments the complex data processing system into distinct modular components: local sensor data collection modules, historical log storage modules, AI analysis modules, and maintenance recommendation modules. Each component handles specific tasks independently, reducing overall system complexity while enabling comprehensive data analysis across multiple machines for improved maintenance accuracy.
3Reliability
If AI algorithms analyze multiple sensor logs and maintenance records to determine priority maintenance tasks, then the effectiveness of maintenance tasks improves, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing historical sensor logs and maintenance records in structured formats before they are needed. The system pre-organizes data by machine type, sensor location, and time periods, and pre-calculates baseline statistics. This preliminary preparation significantly reduces the time and computational resources required when AI algorithms need to analyze the data for determining priority maintenance tasks.
Data Source
AI summary
Systems, methods, non-transitory computer readable media can be configured to accessing a target sensor log corresponding to a first machine; accessing one or more prior sensor logs corresponding to the first machine and one or more prior sensor logs corresponding to a plurality of second machines which are of the same type as the first machine; accessing a plurality of computer readable logs corresponding to the first machine and the second machines, the computer readable logs for each second machine comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determining a set of statistical metrics characterising a difference between the target sensor log and each prior sensor log; selecting a sub-set of the prior sensor logs in dependence upon the statistical metrics; analysing the maintenance logs to correlate each prior sensor log included in the subset to one or more correlated maintenance tasks; selecting a priority maintenance task based on the sub-set of prior sensor logs, t and the correlated maintenance tasks; and outputting the priority maintenance task.