Machine State Prediction From Event Logs for Real-Time Failure Response
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Solution Overview
Problem
Industrial machines face challenges in predicting failures due to constraints related to data quality and quantity, recognition and reaction time, processing delays, accuracy of data processing, and maintenance availability, which impact efficiency and effectiveness in monitoring and maintaining these machines.
Innovation Solution
A computer-implemented method generates a prediction model using event logs from industrial machines with common properties, extracting features, clustering them into vector clusters, and assigning machine states to provide transition probabilities, optimizing for real-time processing and minimizing delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators manually decode and analyze event log codes to predict machine failures, then interpretation accuracy can be maintained, but the time required for recognition and reaction increases significantly
Solution Approach 1:
The patent replaces the mechanical human cognitive process of decoding event logs with an automated computer-based system using machine learning algorithms. The system automatically processes event codes, extracts features, and predicts machine failures without human intervention in the decoding process, thereby maintaining accuracy while dramatically reducing recognition and reaction time.
Solution Approach 2:
The patent introduces an intermediary automated analysis system that sits between the raw event logs and the human operator. This intermediary system decodes event codes, extracts relevant features, and presents processed information to operators, eliminating the time-consuming manual decoding step while preserving the ability to make accurate failure predictions.
2Loss of time
If the computer processes event data in real-time with minimized delays, then reaction time before error occurrence is reduced, but processing complexity and computational resources increase
Solution Approach 1:
The patent segments the event data processing into distinct modular components: event code decoding, feature extraction, feature vector generation, and prediction modeling. Each module handles a specific aspect of processing independently, which reduces overall system complexity while enabling real-time processing through parallel computation of different feature aspects.
Solution Approach 2:
The patent transforms raw event codes into standardized feature vectors with specific parameter formats. By changing the parameter representation from opaque codes to structured numerical features, the system enables faster computational processing while maintaining the information needed for accurate real-time predictions.
3Measurement precision
If the system processes all available event data to improve prediction accuracy, then false alarms may increase, but if data processing is more selective, then accuracy may decrease
Solution Approach 1:
The patent extracts only the most relevant features from the complete event data through a feature selection process. The system identifies and extracts discriminative features that are most indicative of actual failures while filtering out noise and irrelevant information. This selective extraction maintains high prediction accuracy while reducing false alarms by focusing on meaningful patterns.
Solution Approach 2:
The patent applies a threshold-based filtering mechanism that processes event data selectively rather than uniformly. By applying partial action—processing only events that exceed certain significance thresholds or match specific patterns—the system avoids triggering false alarms from minor variations while still capturing all genuine failure indicators.
4Reliability
If industrial machines are stopped for maintenance at any time, then maintenance can be performed immediately, but productivity and technical resource efficiency decrease
Solution Approach 1:
The patent performs preliminary analysis of event data to predict machine failures before they actually occur. By detecting early signs of degradation and predicting future failures, the system enables maintenance to be scheduled in advance during planned downtime periods rather than requiring immediate unplanned stoppages, thus maintaining productivity while ensuring maintenance availability.
Solution Approach 2:
The patent implements a feedback loop where prediction results are continuously monitored and used to adjust maintenance scheduling. The system provides feedback about machine health status and predicted failure timelines, enabling operators to optimize maintenance timing to coincide with natural production breaks or lower-demand periods, thereby balancing maintenance needs with productivity requirements.
Data Source
AI summary
Disclosed is a method for generating a prediction model. The model can be used in processing machine event data to predict behavior of a plurality of industrial machines under supervision. The prediction model can be configured to determine current and future states of the industrial machines. The method can include extracting event features from event codes and structuring the event features into feature vectors. A first dimension of a first feature vector corresponds to a first event feature, and a second dimension of the first feature vector corresponds to a second event feature. The method can also include generating the prediction model by clustering the feature vectors into a plurality of vector clusters, the vector clusters assigned to respective machine states. The prediction model can be constructed based on event data from a first industrial machine and be applied to control an operating state of a second industrial machine.