Machine Failure Prediction Using Low- and High-Fidelity Models
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Solution Overview
Problem
Existing systems for predicting machine failure in manufacturing production lines are often complex, difficult to operate, and inefficient, failing to effectively predict failures and resulting in unplanned downtime.
Innovation Solution
A system comprising a controller, sensors, a plugin device, and multiple computing devices that collect and process data using low and high fidelity models to determine machine failure predictions, transmitting and displaying these predictions to minimize downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing systems for predicting machine failure are used, then machine failure prediction is provided, but the systems are complex, difficult to operate, and inefficient
Solution Approach 1:
The system segments the machine failure prediction process into distinct functional modules: data collection module, data processing module, prediction model module, and output module. Each module performs a specific function, making the overall complex system manageable and easier to operate while maintaining prediction accuracy
Solution Approach 2:
The patent introduces intermediary components such as the plugin device that interfaces between the machine controller and computing devices, and intermediate data structures that bridge raw sensor data and prediction models. These intermediaries simplify operations by providing standardized interfaces and data formats
2Reliability
If existing systems for predicting machine failure are used, then machine failure prediction is provided, but the systems are inefficient
Solution Approach 1:
The system performs preliminary data processing and filtering before feeding data into prediction models. Sensors continuously collect and pre-process data, identifying anomalies and trends in advance, which accelerates the prediction process and improves efficiency without sacrificing accuracy
Solution Approach 2:
The patent implements continuous data collection and processing operations. Sensors continuously monitor machine parameters, and the system continuously updates predictions, ensuring that failure warnings are generated promptly when anomalies detected, thereby improving prediction efficiency and responsiveness
3Measurement precision
If multiple computing devices with low and high fidelity models are used, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the computing system into multiple devices with specialized roles: first computing device runs low-fidelity models for initial analysis, second computing device runs high-fidelity models for detailed prediction. This segmentation allows each device to be optimized for its specific function, improving overall precision while making the complex multi-device architecture more manageable
Solution Approach 2:
The system transitions from a single-dimension approach (single model) to a multi-dimensional approach by implementing both low-fidelity and high-fidelity models across different computing devices. This dimensional expansion in the model fidelity space enables more accurate predictions while distributing computational complexity across multiple devices
Data Source
AI summary
A system for predicting machine failure may include a controller for controlling a machine, a plurality of sensors, a plugin device, a first computing device, a second computing device, and/or a third computing device. The sensors and/or the plugin device may be communicatively coupled to the controller. The first computing device may be communicatively coupled to the plugin device. The plugin device may transmit data associated with the machine to the first computing device. The first computing device may execute at least one low fidelity model to determine an interesting event associated with the machine. The second computing device may be communicatively coupled to the first computing device. The first computing device may transmit data associated with the interesting event to the second computing device. The second computing device may execute at least one high fidelity model to determine a machine failure prediction.