Reliability Pattern Classification Using Visual Deep Learning
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Solution Overview
Problem
Existing methods for identifying failure modes and reliability patterns in complex systems are inadequate, leading to false positives and negatives, manual expert intervention, and inefficiencies in large-scale maintenance, particularly due to the need for qualitative characterization and handling variable-length data.
Innovation Solution
A vision-based deep learning system that creates visual representations of raw data and uses a machine learning model to identify reliability patterns, categorize failure modes, and implement responsive actions without altering the data, enabling scalable and accurate qualitative characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If simple aggregate parameters such as MTBF are used, then computational cost and memory requirements are reduced, but information about underlying reliability behavior is lost
Solution Approach 1:
The patent creates visual representations (copies) of the raw reliability data that preserve all original information while enabling efficient processing. These visual representations serve as intermediate structures that maintain data fidelity without requiring computation on the full raw datasets, thus resolving the contradiction between computational efficiency and information preservation.
2Reliability
If non-parametric models are used, then rich empirical models are obtained regardless of data amount, but computational expense increases and models become difficult to characterize
Solution Approach 1:
The patent extracts the essential visual patterns from raw data that capture reliability behavior without requiring full non-parametric modeling. By taking out only the critical visual features needed for classification, the system achieves high accuracy while avoiding the computational burden of complete non-parametric models.
3Use of energy by stationary object
If parametric models are used, then qualitative characterization is enabled and computation is inexpensive, but models are sensitive to data amount and require expert validation
Solution Approach 1:
The patent creates visual representations that serve as robust intermediaries between raw data and parametric models. These visual copies provide stable input that reduces sensitivity to data quantity variations, enabling parametric models to perform reliably without requiring extensive expert validation while maintaining low computational cost.
4Ease of manufacture
If known machine learning algorithms require fixed length input features, then data processing is simplified, but information is lost through truncation or noise is introduced through padding
Solution Approach 1:
The patent transforms variable-length raw data into fixed-dimensional visual representations by adding a visual dimension. This dimensional transformation allows the system to maintain all original information while achieving the fixed-length input requirements of standard machine learning algorithms, eliminating the need for truncation or padding.
5Measurement precision
If manual expert inspection is used, then accurate reliability labels are obtained, but the process is untenable at large scales
Solution Approach 1:
The patent introduces visual representations as an intermediary between raw data and expert analysis. These visual intermediaries capture the essence of reliability patterns in a form that can be automatically processed by machine learning models while preserving the qualitative insights that experts would extract, thereby enabling scalable processing without sacrificing accuracy.
6Adaptability or versatility
If aggregation based on data distribution is used, then fixed-length input is achieved, but information loss occurs
Solution Approach 1:
The patent resolves this contradiction by transforming data into a visual dimension that naturally accommodates variable lengths while maintaining information integrity. This visual representation approach provides the adaptability needed for different data distributions without requiring aggregation that would cause information loss.
Data Source
AI summary
A reliability pattern classification system includes a communication device configured to obtain historical data indicative of usage of a component of a powered system, and a control unit that can create a visual representation of the historical data. The control unit also can identify one or more reliability patterns within the visual representation using a vision-based, deep learning model, categorize a failure mode of the component based on the one or more reliability patterns that are identified, and implement one or more responsive actions to change a state of condition of the component, the powered system, or both the component and the powered system.