Unlabeled Sensor Anomaly Detection for Real-Time Device Failure
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Solution Overview
Problem
Traditional machine learning approaches fail to detect device failures in real-time due to the absence of annotated data and supporting information in industrial settings, making it challenging to identify abnormal behavior from unlabeled sensor observations.
Innovation Solution
A processor-implemented method using a stacked auto-encoder with optimized hyperparameters, applying a windowing technique to create a windowed dataset, followed by dimensionality reduction and density-based clustering to predict the operating status of devices as success, failure, or transition states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning approaches are used for device failure detection, then annotated data and domain expertise are required, but such data and expertise are absent in industrial settings
Solution Approach 1:
Instead of requiring annotated data to train models (traditional approach), the patent inverts the approach by using unlabeled sensor data and having the model learn normal patterns automatically. The auto-encoder learns to reconstruct normal sensor data, and deviations from reconstruction indicate anomalies, eliminating the need for labeled failure data.
Solution Approach 2:
The system performs self-service by automatically learning device behavior patterns from unlabeled sensor data without requiring domain expertise or manual annotation. The stacked auto-encoder and clustering algorithms autonomously identify normal operating patterns and detect deviations, making the system self-sufficient in industrial settings.
2Measurement precision
If complex deep learning models are applied to unlabeled sensor data, then detection accuracy improves, but computational power requirements increase
Solution Approach 1:
The patent segments the complex detection task into multiple manageable components: (1) stacked auto-encoder for feature extraction from sensor data, (2) dimensionality reduction for compact representation, and (3) clustering for pattern recognition. This segmentation allows each component to be computationally efficient while maintaining high overall detection accuracy.
Solution Approach 2:
The patent transforms high-dimensional sensor data into lower-dimensional latent representations through the auto-encoder and dimensionality reduction steps. This dimensionality change reduces computational complexity and memory requirements while preserving the essential features needed for accurate anomaly detection.
Data Source
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AI summary
Conventionally, detecting time when a device is going to fail in real time has been a real challenge given the associated constraints and requirements. Due to absence in any supporting information or annotated data, traditional approaches have failed to detection abnormality in devices. Present disclosure provide systems and methods for detecting abnormal behaviour of a device from associated unlabeled sensor observations wherein KPIs are computed based on unlabeled sensor observations of at least two sensor parameters and windowing technique is applied on modified dataset to obtain windowed dataset based on which hyper-parameters of deep learning-based auto-encoder are optimized to obtain set of embeddings. Dimensionality reduction technique is applied on the embeddings to obtain embeddings with reduced dimension. Density based clustering technique with hyper-parameters is applied on embeddings with reduced dimension and clusters) for unlabeled sensor observations are obtained. Cardinality is assigned to clusters) to predict abnormal behaviour of the device.