Machine Tool Sensor Diagnosis via Feedback Pulse Counting
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Solution Overview
Problem
Current machine tool control systems require external measurement devices to diagnose sensor malfunctions, which is time-consuming and costly, and cannot distinguish between wire connection errors and noise or sensor issues based on feedback pulse count anomalies.
Innovation Solution
A machine tool control device that calculates feedback pulse counts from A- and B-phase signals and one-rotation signals to determine anomalies, using a feedback counter, storage units, and an anomaly cause determination unit to differentiate between noise and sensor errors by comparing count values to reference values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external measurement devices such as oscilloscope and computer tool are used to measure waveforms of sensor signals, then the cause of sensor malfunction can be analyzed, but much time and cost are required
Solution Approach 1:
The control device performs self-diagnosis by using its own internal resources (CPU, feedback counter, storage units) to monitor sensor signals and detect malfunctions. The system serves itself by analyzing feedback pulse counts between one-rotation signals without requiring external measurement devices, thereby reducing both time and cost while maintaining diagnostic capability
Solution Approach 2:
The invention extracts only the essential diagnostic information (feedback pulse count between one-rotation signals) from the complex sensor signal waveform. By focusing on this specific parameter rather than analyzing entire waveforms with external devices, the system achieves effective malfunction detection with simplified processing using internal resources
2Reliability
If feedback pulse count anomaly is detected between one-rotation signals, then sensor malfunction can be detected, but the specific cause (wire connection error vs. noise vs. sensor issue) cannot be determined
Solution Approach 1:
The anomaly cause determination unit segments the diagnostic process into distinct evaluation stages: first detecting feedback pulse count anomalies, then analyzing the pattern and characteristics of the anomaly to differentiate between wire connection errors, noise, and sensor issues. This segmented approach transforms a single undifferentiated detection into a multi-stage diagnostic process that identifies specific causes
Solution Approach 2:
Instead of trying to directly identify the cause from a single anomaly detection, the system inverts the approach by using the absence of certain anomaly patterns to rule out specific causes. By analyzing what the anomaly is NOT (e.g., not consistent with wire connection error patterns), the system can positively identify the remaining cause through elimination
3Ease of operation
If all anomalous count values are determined as wire connection errors, then anomaly detection is simple, but the specific cause of the anomaly cannot be distinguished
Solution Approach 1:
The system dynamically adjusts the diagnostic approach based on the characteristics of the detected anomaly. Rather than using a static rule that labels all anomalies as wire connection errors, the anomaly cause determination unit evaluates the nature, magnitude, and pattern of the anomaly to dynamically determine the most likely cause, thereby maintaining simplicity while improving accuracy
Data Source
AI summary
A machine tool control device according to the present invention includes a feedback counter for obtaining A- and B-phase signals of rectangular waves or sine waves and a one-rotation signal, which are outputted from a sensor for detecting the position or speed of a driven axis or a motor, to calculate a feedback count value that is a count value of the number of feedback pulses generated from the A- and B-phase signals; a feedback count value storage unit for storing an inter-one-rotation-signal feedback count value that is the feedback count value counted between the two sequential one-rotation signals; a reference value storage unit for storing an anomaly determination reference value corresponding to the inter-one-rotation-signal feedback count value; and an anomaly cause determination unit for determining the cause of an anomaly by comparison between the inter-one-rotation-signal feedback count value and the anomaly determination reference value.