Predictive Storage Management Using Neural Network Anomaly Detection
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Solution Overview
Problem
Autonomous vehicles lack effective predictive maintenance for their components, leading to potential breakdowns during operation, which can be hazardous and inconvenient, as existing systems do not adequately utilize sensor data for proactive maintenance scheduling.
Innovation Solution
A data storage device equipped with an artificial neural network (ANN) that processes sensor data from various sources to predict component failures, using unsupervised machine learning to recognize normal patterns and detect anomalies, thereby scheduling maintenance services proactively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is collected and processed to predict component failures, then reliability of vehicle operation is improved, but device complexity increases due to additional computational systems
Solution Approach 1:
The system performs preliminary actions by continuously collecting sensor data and training machine learning models in advance to predict component failures before they occur. The model is trained using historical failure data and operational parameters to identify patterns that precede failures, enabling proactive maintenance scheduling.
Solution Approach 2:
The computational system performs self-service by automatically monitoring its own components, detecting anomalies, and predicting failures without requiring external intervention. The system self-diagnoses and schedules its own maintenance needs, reducing the need for manual inspection and intervention.
2Measurement precision
If machine learning models are trained using historical failure data, then prediction accuracy is improved, but loss of information increases due to data processing requirements
Solution Approach 1:
The system extracts relevant features and patterns from large volumes of sensor data by applying machine learning algorithms. It selectively extracts meaningful information about component health trends, failure patterns, and operational parameters, filtering out redundant or irrelevant data to reduce information loss during processing.
Solution Approach 2:
The system implements feedback mechanisms where prediction results are fed back into the monitoring system to refine future predictions. Historical failure data and actual component performance are continuously used to retrain and improve the machine learning models, enhancing prediction accuracy over time while adapting to changing operational conditions.
Data Source
AI summary
Systems, methods and apparatus of predictive management of failing portions of data storage media in a data storage device. For example, the data storage device can include: one or more storage media components; a controller configured to store data into, and retrieve data from, a portion of the one or more storage media components; and an artificial neural network configured to receive, as input from the controller, parameters relevant to health of the portion and generate an anomaly classification based on the input. The controller can be configured to adjust a data storage usage of the portion in response to the anomaly classification. For example, the controller can improve data reliability operations to reduce the likelihood of data loss and/or catastrophic failure.