Machine Behavioral Models for Ambiguous Failure Prediction
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Solution Overview
Problem
Existing monitoring systems for industrial machines are inefficient in predicting failures, often identifying issues only after downtime begins, relying on predetermined rules that ignore vast amounts of collected data, leading to wasted resources and premature maintenance, and requiring specialized operators, which can introduce errors and increase costs.
Innovation Solution
A method utilizing reinforcement learning to identify ambiguous segments in machine behavioral models, generating queries to client devices for input, and updating machine learning algorithms to associate corrective solutions based on similarity thresholds, optimizing the monitoring of industrial machine operations and predicting failures in a timely manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing monitoring systems use predetermined rules to check key parameters, then the system complexity is reduced and ease of operation is improved, but the measurement precision and reliability of failure prediction deteriorate because vast amounts of collected data are ignored
Solution Approach 1:
The patent creates a virtual copy of the machine's operational state through digital twins and behavioral models. These models replicate the machine's behavior patterns, allowing analysis of vast amounts of data without directly complicating the physical monitoring system. The digital copy processes comprehensive data while the physical system remains operationally simple.
Solution Approach 2:
The patent introduces machine learning algorithms and behavioral models as intermediaries between the raw sensor data and the monitoring system's decision-making. These intermediaries process and interpret vast amounts of collected data, extracting meaningful patterns without requiring the operators to directly analyze the raw data, thus maintaining ease of operation while improving measurement precision.
2Device complexity
If existing systems rely on periodic testing at predetermined intervals, then the device complexity is reduced, but the loss of time increases due to premature maintenance requests and failures not being prevented
Solution Approach 1:
The patent transitions from static periodic testing to dynamic continuous monitoring. The system continuously analyzes machine behavioral data and adjusts monitoring intensity based on detected patterns and anomalies. This dynamic approach allows the system to maintain low complexity while preventing failures in real-time, avoiding both premature maintenance and unexpected downtime.
Solution Approach 2:
The patent performs preliminary analysis of machine behavioral patterns to predict failures before they occur. By continuously monitoring and analyzing data trends, the system identifies early warning signs and takes preventive action before failures happen, eliminating the need for reactive periodic testing and reducing downtime loss.
3Measurement precision
If existing monitoring systems require specialized operators for dedicated testing equipment, then the measurement precision may be maintained, but the ease of operation deteriorates and human error increases
Solution Approach 1:
The patent enables the monitoring system to perform self-diagnosis and self-analysis through automated machine learning algorithms. The system independently processes sensor data, identifies patterns, and predicts failures without requiring specialized human operators. This self-service capability maintains measurement precision while dramatically improving ease of operation and eliminating human error.
Solution Approach 2:
The patent replaces the mechanical need for specialized human operators with automated computational systems. Machine learning algorithms and behavioral models perform the analysis functions previously requiring human expertise, substituting human cognitive processes with computational processes that maintain precision while improving operational ease.
4Ease of manufacture
If existing solutions use predetermined rule sets provided by engineers, then the ease of manufacture is improved, but the adaptability deteriorates because the rules do not account for all collected data
Solution Approach 1:
The patent performs preliminary training of machine learning models using historical machine data during the manufacturing or deployment phase. This preliminary action creates adaptive models that can handle diverse scenarios without requiring manual rule creation for each case, combining ease of manufacture with high adaptability.
Solution Approach 2:
The patent uses machine learning models that automatically adjust parameters and thresholds based on the specific machine being monitored. Instead of fixed predetermined rules, the system adapts its parameters to match the unique characteristics of each machine, providing both ease of manufacture through standardized deployment and high adaptability to individual machines.
Data Source
AI summary
A system and method for a method for optimizing machine learning algorithms for monitoring industrial machine operation, including: monitoring at least one industrial machine behavioral model of at least one industrial machine; identifying at least a first ambiguous segment of the at least one industrial machine behavioral model having a first set of characteristics, and identifying a corrective solution recommendation associated with the first ambiguous segment; identifying at least a second ambiguous segment of the at least one industrial machine behavioral model having a second set of characteristics; determining if a similarity between the first set of characteristics and the second set of characteristics exceed a predetermined threshold; and updating a machine learning algorithm of the at least one industrial machine behavioral model to associate the corrective solution recommendation to the second ambiguous segment when it is determined that the similarity has exceed the predetermined threshold.