Industrial Signal Data Clustering for Condition Recognition
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Solution Overview
Problem
Machine learning techniques for condition recognition in complex industrial systems face challenges due to the need for large, well-correlated training data sets, which become inadequate when environmental or system changes occur, leading to inconsistent predictions.
Innovation Solution
A computer-implemented method and system that processes signal data from sensors to generate a signal data model by aggregating data into feature vectors, clustering, and assigning classification labels, allowing for continuous assessment and reporting of machine conditions, even with evolving environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning techniques are implemented for condition recognition, then condition identification capability is improved, but the system requires large, well-correlated training data sets which become inadequate when environmental or system changes occur
Solution Approach 1:
The patent implements dynamic adaptation by allowing the system to learn from new data continuously. The machine learning model is updated with new training data as it becomes available, enabling the system to adapt to changing environmental conditions and system variations over time, resolving the contradiction between initial training data requirements and ongoing adaptability.
Solution Approach 2:
The system incorporates feedback mechanisms where prediction results and new sensor data are fed back into the training process. This allows the model to continuously improve and adapt to new conditions, maintaining reliability while gaining versatility across changing environments through iterative learning from actual system performance.
2Measurement precision
If a well-formed training data set is used to define conditions, then initial prediction accuracy is improved, but the system cannot consistently predict conditions when the environment or system changes over time
Solution Approach 1:
The system performs preliminary actions by continuously collecting and preparing new training data in advance. Rather than relying solely on initial training data, the system proactively gathers new data and updates models before performance degradation occurs, maintaining both accuracy and long-term consistency.
Solution Approach 2:
The patent ensures continuous learning and model updating operations. The system continuously ingests new sensor data, retrains models, and deploys updated predictions without interruption, maintaining prediction consistency over extended periods by ensuring the learning process never stops.
3Reliability
If machine learning algorithms are trained with sufficient data, then condition recognition results are improved, but the complexity of collecting and maintaining training data increases
Solution Approach 1:
The system implements self-service by automatically collecting, preprocessing, and managing training data without extensive manual intervention. The machine learning pipeline autonomously handles data collection from sensors, feature extraction, model training, and deployment, reducing the operational complexity of maintaining large training data sets while preserving recognition reliability.
Data Source
AI summary
A method for determining specific conditions occurring on industrial equipment based upon received signal data from sensors attached to the industrial equipment is provided. Using a server computer system, signal data is received and aggregated into feature vectors. Feature vectors represent a set of signal data over a particular range of time. The feature vectors are clustered into subsets of feature vectors based upon attributes the feature vectors. One or more sample episodes are received, where a sample episode includes sample feature vectors and specific classification labels assigned to the sample feature vectors. A signal data model is created that includes the associated feature vectors, clusters, and assigned classification labels. The signal data model is used to assign classification labels to newly received signal data using the mapping information for the existing feature vectors, existing clusters and associated classification labels to determine the specific conditions occurring on the industrial equipment.