Predictive Maintenance Modeling Using Precursor Signal Detection
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Solution Overview
Problem
Existing systems lack effective methods for predicting maintenance events in complex machinery, such as ships and construction equipment, based on historical maintenance data and sensor logs, leading to inefficiencies in maintenance scheduling and resource management.
Innovation Solution
A method and system that utilizes historic maintenance data and dynamic time warping to identify precursor signals of maintenance events, allowing for the prediction of future occurrences by comparing data from multiple resources and calculating probabilities of future events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional maintenance scheduling methods are used, then maintenance can be performed, but maintenance timing is not optimized leading to increased downtime and reduced productivity
Solution Approach 1:
The system performs preliminary analysis of historical maintenance data and sensor logs to identify precursor signals before maintenance events occur. By detecting patterns and anomalies in advance, the system enables proactive maintenance scheduling, allowing organizations to plan maintenance activities optimally rather than reacting to failures or following fixed schedules, thereby reducing downtime and improving productivity
Solution Approach 2:
The system continuously monitors sensor data and compares it against historical maintenance records, creating a feedback loop that refines maintenance predictions over time. This feedback mechanism allows the system to learn from actual maintenance outcomes and adjust future predictions, optimizing maintenance timing to minimize downtime while maintaining equipment reliability
2Reliability
If maintenance data is collected and analyzed, then predictive capability improves, but system complexity increases
Solution Approach 1:
The system extracts and isolates specific precursor signals from large volumes of sensor data and maintenance records. By focusing on key indicators rather than analyzing all data points equally, the system achieves reliable predictions without requiring proportionally complex processing infrastructure. The extraction of meaningful patterns from raw data reduces the effective complexity while maintaining prediction accuracy
Solution Approach 2:
The system introduces intermediary processing layers including data normalization, feature extraction, and pattern recognition algorithms that bridge raw sensor data and maintenance predictions. These intermediary steps simplify the overall system architecture by breaking down complex analysis into manageable stages, making the system more tractable while improving prediction reliability
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can be configured to perform receiving a notification of a maintenance event associated with a resource. The method includes retrieving historic maintenance data in relation to the resource with which the fault is associated, the maintenance information originating from a time period preceding the time of the maintenance event. The method includes identifying at least a portion of the retrieved historic maintenance data as being indicative of the maintenance event. The method also includes causing the portion of the retrieved historic maintenance data identified as being indicative of the maintenance event to be stored as a precursor signal of the maintenance event. The method also includes causing future maintenance data received from a plurality of resources related to the resource with which the maintenance event is associated to be monitored to predict a future occurrence of the maintenance event in the plurality of resources.