Cutting Spindle Anomaly Detection Using Power-Based State Separation
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Solution Overview
Problem
Existing methods for determining spindle anomalies in cutting machines with electrically driven spindles are inefficient, often requiring unavailable information and lacking precision, especially when differentiating between cutting and idling operations, which can lead to unexpected spindle failures and downtime in integrated production processes.
Innovation Solution
A method involving the determination of electric power consumption during working cycles, divided into time windows, where the spindle's cutting and idling states are differentiated using Gaussian Mixture Models, and median and expected deviation values are calculated to identify anomalies, allowing for precise detection of impending spindle failures without prior knowledge of operational states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning techniques are used to detect spindle anomalies based on operational data, then anomaly detection capability is improved, but the method requires unavailable information such as whether the spindle is cutting or rotating idly
Solution Approach 1:
The system uses the electric power consumption data itself to automatically determine the operational state (cutting or idling) without requiring external information. The Gaussian Mixture Model analyzes the power consumption patterns to self-identify whether the spindle is cutting or rotating idly, making the system self-sufficient and eliminating the need for unavailable operational state information.
Solution Approach 2:
The invention changes the approach from using operational state as input to using electric power consumption patterns as the primary parameter. By analyzing variations in power consumption and applying statistical models, the system derives operational state information from the power data itself, transforming the problem from information-deficient to information-sufficient.
2Reliability
If spindle replacement is performed to ensure continuous operation, then reliability is improved, but production time is lost during spindle replacement
Solution Approach 1:
The system performs preliminary anomaly detection and warns operators before the spindle actually fails. By detecting abnormal power consumption patterns and predicting impending failures, the system enables planned maintenance scheduling, allowing spindle replacement to be performed during scheduled downtime rather than causing unexpected production stoppages.
Solution Approach 2:
The system continuously monitors electric power consumption and provides feedback about spindle health status. This real-time feedback allows operators to track spindle condition, predict failures, and schedule replacements optimally, balancing reliability maintenance with production continuity.
3Device complexity
If electric power consumption data is analyzed without differentiating cutting and idling states, then processing complexity is reduced, but anomaly detection precision deteriorates
Solution Approach 1:
The system segments the operational data into distinct categories (cutting and idling states) using the Gaussian Mixture Model. By dividing the power consumption data into state-specific groups, the system can analyze each state separately with appropriate thresholds, significantly improving anomaly detection precision while managing complexity through automated classification.
Solution Approach 2:
The invention replaces manual or complex mechanical differentiation methods with statistical modeling and machine learning algorithms. The Gaussian Mixture Model automatically distinguishes between cutting and idling states based on power consumption patterns, achieving high precision without requiring complex physical sensors or manual intervention.
Data Source
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AI summary
Present invention concerns a method (200) for anomaly detection in a cutting machine (105) with a spindle (115) which is driven by an electric motor (120). The proposed method (200) comprises steps of determining (215) an electric power consumption of the motor (120) during a working cycle (305) of the machine (105); dividing the working cycle into time windows (335) ; for each time window (335), determining (225) wether the spindle (115) was cutting and/or idling on the basis of associated electric power values; determining (245) median and expected deviation of electric power values for cutting and idling operation over the sequence of time windows (335) ; and determining (250) an anomaly if electric power values during cutting or idling exceed a predetermined relationship to the corresponding median and expected deviation values.