Histogram-Based Anomaly Detection for Noisy Machine Telematics
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Solution Overview
Problem
Existing systems face challenges in accurately detecting anomalous operating characteristics in industrial machines due to noise in high-frequency sensor data and time series data, often misidentifying normal behavior as anomalous or overlooking minor irregularities that could lead to machine failure.
Innovation Solution
A method involving the collection of telematics data, generation of histograms, and application of a histogram comparator engine to determine anomalous operating characteristics, which compares the data against reference data to identify deviations and present specific recommendations or predictions to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high frequency sensor data is collected for anomaly detection, then detection sensitivity is improved, but noise in the data increases making it difficult to differentiate normal behavior from anomalies
Solution Approach 1:
The patent segments the continuous high frequency sensor data into discrete histogram bins, transforming the raw time series data into a structured format that separates signal from noise. This segmentation allows the system to analyze data in manageable intervals while maintaining the ability to detect anomalies.
Solution Approach 2:
The patent introduces histograms as an intermediary representation between the raw sensor data and the anomaly detection algorithm. By converting raw data into histogram distributions and comparing these distributions, the system effectively filters noise while preserving anomaly detection capability.
2Productivity
If traditional anomaly detection methods are used on high frequency data, then detection speed is maintained, but false positives increase where normal behavior is misidentified as anomalous
Solution Approach 1:
The patent performs preliminary actions by pre-defining histogram bins and reference distributions before anomaly detection occurs. This preprocessing step establishes a framework that guides subsequent analysis, enabling fast comparison operations while reducing false positives through structured data organization.
Solution Approach 2:
The patent transforms the detection parameters by converting raw sensor values into histogram frequency distributions. This parameter transformation changes the nature of the data being analyzed, making it more suitable for robust anomaly detection that is less sensitive to noise and transient variations.
3Measurement precision
If detailed analysis of minor irregularities is performed, then detection accuracy for early anomalies is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by focusing computational resources on the most critical aspects of anomaly detection through histogram analysis. Rather than analyzing every detail of the raw data, the system concentrates on distribution patterns that are most indicative of anomalies, achieving high detection accuracy with reduced computational overhead.
Data Source
AI summary
The present technology includes a method for detecting one or more anomalous operating characteristic of an industrial machine. The method can include collecting telematics data indicative of the industrial machine's performance, generating a histogram based on at least a portion of the collected telematics data, applying a histogram comparator engine to the histogram to determine whether the histogram indicates an anomalous operating characteristic, and if the histogram is determined to indicate an anomalous operating characteristic, presenting, to a user, information associated with the anomalous operating characteristic.