Predictive Maintenance Scheduling Using LSTM Device State Sequences
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Solution Overview
Problem
Existing maintenance scheduling methods for devices often fail to anticipate and react to unexpected maintenance requirements, leading to interruptions in device availability and increased costs due to mis-scheduling.
Innovation Solution
A computer-implemented method using a recurrent neural network, specifically a long short-term memory (LSTM), to predict maintenance tasks by analyzing a series of transactions from a sequential transactional database, allowing for proactive scheduling based on predicted state changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a predetermined maintenance schedule is used, then maintenance can be planned in advance, but it fails to react to unexpected maintenance requirements occurring outside the schedule
Solution Approach 1:
The maintenance scheduling system transitions from a static predetermined schedule to a dynamic predictive model that continuously learns from device state changes and adapts to unexpected maintenance requirements. The recurrent neural network processes sequential device data in real-time, enabling the system to adjust maintenance timing based on actual device conditions rather than fixed intervals.
Solution Approach 2:
The system implements feedback by continuously monitoring device state changes and using this information to refine maintenance predictions. The recurrent neural network processes ongoing device data streams, comparing actual device behavior against learned patterns to predict future maintenance needs, thereby creating a closed-loop system that responds to actual device conditions.
2Ease of operation
If maintenance is performed according to a fixed schedule, then service planning is simplified, but unexpected maintenance causes interruptions to device availability and service
Solution Approach 1:
The system performs preliminary actions by predicting maintenance requirements before they actually occur. The recurrent neural network analyzes device state changes and predicts future maintenance needs, allowing maintenance to be scheduled proactively rather than reactively, thus preventing unexpected interruptions to device availability.
Solution Approach 2:
The maintenance schedule becomes dynamic and adaptive rather than fixed. The system continuously updates predictions based on actual device performance data, allowing maintenance timing to flex according to real device conditions while maintaining operational simplicity through automated predictions.
3Ease of manufacture
If traditional maintenance scheduling is used, then implementation is straightforward, but it leads to mis-scheduling and increased costs
Solution Approach 1:
The patent replaces traditional mechanical/mmanual scheduling methods with an intelligent predictive system based on recurrent neural networks. This substitution automates the complex analysis of device state changes and maintenance pattern recognition, achieving high scheduling accuracy while maintaining implementation feasibility through standardized machine learning workflows.
Solution Approach 2:
The system enables self-service by allowing the maintenance scheduling to be automatically generated and optimized by the recurrent neural network without requiring extensive manual intervention. The model learns from device data and autonomously predicts maintenance needs, reducing the complexity of implementation while improving reliability.
Data Source
AI summary
A computer implemented method of scheduling maintenance tasks for a target device, the device having a unique device type, comprising: retrieving, from a sequential transactional database, a series of transactions for each of a plurality of devices having the device type, each transaction corresponding to a change of state of one of the other devices; training a recurrent neural network based on each retrieved series of transactions as a predictor of a subsequent change of state of a device having the device type; retrieving, from a sequential transactional database, a series of transactions for the target device, each transaction corresponding to a change of state of the device; predicting a next maintenance task for the target device based on the series of transactions for the target device applied to the recurrent neural network; scheduling the predicted next maintenance task for the target device.