Centrifugal Pump Blockage Detection via Fourier Spectral Analysis
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Solution Overview
Problem
Current methods for detecting operating states and blockages in centrifugal pumps are not reliable or timely, leading to potential damage from solids content, which can cause clogging and inefficiency.
Innovation Solution
A method involving Fourier transformation of acceleration, pressure, and motor current signals to obtain frequency spectra, followed by analysis using a machine-learning trained image analysis algorithm to recognize spectral patterns, allowing for early detection of blockages and maintenance needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional detection methods are used for centrifugal pump blockages, then the system structure remains simple, but the detection reliability and timeliness deteriorate
Solution Approach 1:
The patent replaces conventional mechanical blockage detection methods with signal processing and machine learning algorithms. Acceleration, pressure, and motor current signals are transformed via Fourier transformation and analyzed using trained image analysis algorithms to detect blockages, eliminating the need for complex mechanical sensors while improving detection reliability.
Solution Approach 2:
The patent introduces spectral patterns as an intermediary between physical pump operations and blockage detection. The machine learning algorithm learns to recognize spectral patterns in transformed signals that correspond to blockage conditions, enabling reliable detection without direct mechanical contact or complex sensing hardware.
2Measurement precision
If signal processing and machine learning analysis are implemented, then blockage detection precision improves, but computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary action by training the image analysis algorithm offline before actual pump operation. The algorithm is pre-trained on spectral patterns from various pump conditions, so during real-time operation, only inference is needed rather than full training, significantly reducing processing time while maintaining high detection precision.
Solution Approach 2:
The patent uses dynamic signal processing by continuously transforming acceleration, pressure, and motor current signals into frequency spectra and spectrograms. This dynamic approach allows real-time adaptation to changing pump conditions while maintaining precise blockage detection through the pre-trained algorithm.
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 reliable and reproducible detection of operating states, including blockages and solids content, allowing for timely intervention to prevent damage and improve pump efficiency.
Implementation Method 1
the acceleration, pressure and/or motor current signal is Fourier-transformed to obtain a frequency spectrum and/or a spectrogram
Data Source
Figure 1
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Figure 3a~3b
AI summary
The invention relates to a method for detecting an operating state of a centrifugal pump (1) comprising a pump housing (2) with a shaft (4) arranged in the pump housing (2), an impeller (6) mounted on the shaft (4) and a motor (5) driving the shaft (4), comprising the steps of: during pumping of liquid with a possible solid content with the centrifugal pump (1), acquiring a temporal acceleration, pressure and/or motor current signal of the centrifugal pump (1), Fourier transforming the acceleration, pressure and/or motor current signal to obtain a frequency spectrum and/or a spectrogram, and analyzing the frequency spectrum and/or the spectrogram with regard to a spectral pattern to detect the operating state.