Motor Startup Current Analysis for Noise-Resistant Fault Detection
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Solution Overview
Problem
Existing failure symptom detection methods for electric motor-provided equipment face challenges in accurately detecting abnormalities due to noise interference from inverter driving and load variations, leading to erroneous signal detection and increased maintenance costs.
Innovation Solution
A failure symptom detection device and method that extracts current data during the acceleration period of an electric motor, performs frequency analysis, and compares intensity values of spectrum peaks in the rotational frequency band to determine abnormality without additional sensors, using existing current sensors to generate accurate diagnostic results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If current and voltage signals are analyzed based on detection outputs from various sensors to detect failure symptoms, then automatic monitoring capability is achieved, but measurement precision deteriorates due to noise from inverter driving and load variations
Solution Approach 1:
The patent segments the current signal analysis by extracting only the acceleration period current characteristics, separating them from the noisy steady-state operation signals. This segmentation allows focused analysis on the clean startup phase signals while excluding inverter-driven noise periods
Solution Approach 2:
The patent performs preliminary frequency analysis on the acceleration period current signals to establish baseline characteristics before normal operation begins. This preliminary action creates reference data that can be compared against future operational states to detect abnormalities
2Measurement precision
If various sensors such as torque meters, acceleration sensors, and temperature sensors are attached to each electric motor for constant monitoring, then measurement capability is improved, but device complexity increases
Solution Approach 1:
The patent makes the existing current sensor serve multiple functions: it not only controls the motor operation but also provides diagnostic data for failure detection. This multi-functionality eliminates the need for separate dedicated sensors while maintaining comprehensive monitoring capability
Solution Approach 2:
The system uses its own operational current signals for self-diagnosis purposes. The motor's normal operating currents contain the information needed for failure detection, allowing the system to monitor itself without external sensing equipment
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate detection of failure symptoms in electric motor-provided equipment while minimizing noise influence and avoiding the need for extra sensors, thereby reducing maintenance costs and improving diagnostic precision.
Implementation Method 1
a frequency analysis unit which performs frequency analysis on each piece of data divided by the data generation unit
Data Source
AI summary
This failure symptom detection device includes: a diagnosis calculation circuitry which calculates index values for abnormality-presence/absence determination for electric motor-provided equipment, from a detection result of current flowing from a driving device to an electric motor; and a diagnosis determination circuitry which determines presence/absence of abnormality of the electric motor-provided equipment from a calculation result of the diagnosis calculation circuitry. The diagnosis calculation circuitry includes a starting current extraction circuitry which, from detected current, extracts current in an acceleration period until a constant rotational speed is reached after starting of the electric motor, a data generation circuitry which divides current data in the acceleration period extracted by the starting current extraction circuitry, into pieces of data, and a frequency analysis circuitry which performs frequency analysis on each piece of data divided by the data generation circuitry.