K-Means Clustering for Sensor Data Time Series Analysis
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Solution Overview
Problem
Analyzing large volumes of real-time sensor data for equipment operation is costly and impractical due to the computational intensity of pattern recognition, particularly in determining optimal operating states and identifying anomalies.
Innovation Solution
The method employs k-means clustering to find an optimal clustering of equipment operating states, using centroids from an initial clustering as proxies to reduce problem size and achieve faster processing, leveraging statistical analysis and heuristic algorithms like Lloyd's Algorithm and k-means++ to identify normal and abnormal operation patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard k-means clustering algorithm is used to analyze large volumes of sensor data, then optimal clustering corresponding to equipment operating states can be found, but computational cost and processing time become excessively high
Solution Approach 1:
The patent segments the large sensor dataset into multiple partitions or batches, processing each segment separately through the k-means clustering algorithm. This divides the computationally intensive task into smaller, more manageable units that can be processed sequentially or in parallel, significantly reducing the time required to analyze the entire dataset while maintaining clustering accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the sensor data before applying k-means clustering, including data normalization, feature selection, and removal of redundant measurements. This preliminary processing reduces the dimensionality and complexity of the input data, allowing the clustering algorithm to operate more efficiently on a refined dataset that retains the essential patterns needed for accurate equipment state identification.
2Reliability
If pattern recognition is applied to large volumes of real-time sensor data, then equipment operation status can be determined, but computational resources required become prohibitively expensive
Solution Approach 1:
The patent extracts and focuses only on the most relevant features and patterns from the large sensor dataset that are critical for determining equipment operation status. By identifying and extracting key indicators of equipment health and performance, the system eliminates the need to process all raw sensor data, thereby reducing computational energy consumption while maintaining reliable equipment monitoring capability.
Solution Approach 2:
The patent changes parameters by transforming raw sensor readings into normalized, standardized values and by adjusting the granularity of analysis based on equipment criticality. This parameter transformation allows the system to achieve reliable monitoring with reduced computational requirements by working with scaled and optimized data representations rather than raw high-volume sensor streams.
3Loss of information
If comprehensive analysis of all sensor data points is performed, then complete understanding of equipment operating states is achieved, but processing complexity increases dramatically
Solution Approach 1:
The patent applies partial action by analyzing a strategically selected subset of sensor data points that are most indicative of equipment operating states, rather than processing every single data point. This approach uses statistical sampling and importance weighting to ensure that the analyzed subset provides sufficient information about equipment conditions without requiring exhaustive processing of the entire dataset, thereby reducing algorithmic complexity while avoiding significant information loss.
Data Source
AI summary
A method using a fast algorithm automated analysis of time series sensor data that can find an optimal clustering value k for k-means analysis by using statistical analysis of the results of clustering for a stated maximal upper value of k.