COMPUTER PROGRAM AND METHOD FOR DETECTING AND PREDICTING VALVE FAILURE IN A RECIPROCATING COMPRESSOR

• US 20100106458A1
• Filed: 10/28/2008
• Published: 04/29/2010
• Est. Priority Date: 10/28/2008
• Status: Abandoned Application

• Current Assignee: Curators of the University of Missouri (University of Missouri)
• Original Assignee: Curators of the University of Missouri (University of Missouri)
• Inventors: Sarangapani, Jagannathan, Leu, Ming C., Ramesh, Raghuram Puthall
• Application Number:
  US12/259,772
• Publication Number:
  US 20100106458A1
• Time in Patent Office: Days
• US Class Current: 702/181
• CPC Class Codes: F04B 49/065 and making use of computers
• CPC Class Codes: F04B 49/10 Other safety measures

Abstract

Embodiments of the present invention provide a method implemented by a computer program for detecting and identifying valve failure in a reciprocating compressor and further for predicting valve failure in the compressor. Embodiments of the present invention detect and predict the valve failure using wavelet analysis, logistic regression, and neural networks. A pressure signal from the valve of the reciprocating compressor presents a non-stationary waveform from which features can be extracted using wavelet packet decomposition. The extracted features, along with temperature data for the valve, are used to train a logistic regression model to classify defective and normal operation of the valve. The wavelet features extracted from the pressure signal are also used to train a neural network model to predict to predict the future trend of the pressure signal of the system, which is used as an indicator for performance assessment and for root cause detection of the compressor valve failures.

Claims

1. A computer program stored on a computer-readable medium for predicting failure of a valve in a reciprocating compressor, the computer program comprising:

a code segment executable by the computer for monitoring a pressure signal produced by the valve of the reciprocating compressor;

a code segment executable by the computer for applying a time-frequency analysis to the pressure signal so as to obtain a pressure waveform;

a code segment executable by the computer for applying a wavelet transform to the pressure waveform so as to perform a feature selection analysis; and

a code segment executable by the computer for training a plurality of neural networks so a s to select a best performing network operable to predict a behavior for the valve of the reciprocating compressor within a predetermined period of time, the code segment for training of the plurality of neural networks including

a code segment executable by the computer for initializing the plurality of neural networks by inputting a portion of the features selected from the feature selection analysis into each of the plurality of networks,

a code segment executable by the computer for applying a gradient descent algorithm to each neural network to obtain a generalized error of the neural network,

a code segment executable by the computer for selecting from each of the neural networks a plurality of high-performing networks,

a code segment executable by the computer for applying a particle swarm optimization to enhance an accuracy of the selected high-performing networks,

a code segment executable by the computer for creating an equal number of high-performing networks by mutating the high-performing networks selected from step (d3) using an evolutionary algorithm, and

a code segment executable by the computer for repeating the code segments for training of the plurality of neural networks until the plurality of neural networks are trained to have a predetermined accuracy rate between an actual value and a desired value.

2. The computer program as claimed in claim 1, wherein the pressure signal is monitored using a sensor operably connected with the reciprocating compressor and the computer.

3. The computer program as claimed in claim 1, wherein the valve is associated with a pressure, a temperature, a peak pressure value per a cycle of compression, and a time to peak pressure per a cycle of compression.

4. The computer program as claimed in claim 1, wherein application of the neural network is operable to predict an energy trend for the reciprocating compressor.

5. The computer program as claimed in claim 1, wherein the features selected are the wavelet energies obtained from application of the wavelet transform.

6. The computer program as claimed in claim 5, wherein the features selected are ranked according to a criterion function.

7. The computer program as claimed in claim 1, wherein the high-performing networks selected from step (d3) are selected based on a mean squared error of the actual and desired values for the network.

8. The computer program as claimed in claim 2, wherein the plurality of neural networks are trained in with approximately one hundred hours of pressure signals obtained from the sensor.

9. A method for detecting and identifying failure of a valve in a reciprocating compressor, the method comprising the steps of:

(a) monitoring a pressure signal produced by the valve of the reciprocating compressor;

(b) applying a time-frequency analysis to the pressure signal so as to obtain a pressure waveform;

(c) applying a wavelet transform to the pressure waveform so as to perform a feature selection analysis; and

(d) training a plurality of neural networks so as to select a best performing network operable to predict a behavior for the valve of the reciprocating compressor within a predetermined period of time, the training of the plurality of neural networks including the steps of

(d1) initializing the plurality of neural networks by inputting a portion of the features selected from the feature selection analysis of step (c) into each of the plurality of networks,

(d2) applying a gradient descent algorithm to each neural network to obtain a generalized error of the neural network,

(d3) selecting from each of the neural networks a plurality of high-performing networks,

(d4) applying a particle swarm optimization to enhance an accuracy of the selected high-performing networks,

(d5) creating an equal number of high-performing networks by mutating the high-performing networks selected from step (d3) using an evolutionary algorithm, and

(d6) repeating steps (d1)-(d5) until the plurality of neural networks are trained to have a predetermined accuracy rate between an actual value and a desired value.

10. The method as claimed in claim 9, wherein the pressure signal is monitored using a sensor operably connected with the reciprocating compressor.

11. The method as claimed in claim 9, wherein the valve is associated with a pressure, a temperature, a peak pressure value per a cycle of compression, and a time to peak pressure per a cycle of compression.

12. The method as claimed in claim 9, wherein application of the neural network is operable to predict an energy trend for the reciprocating compressor.

13. The method as claimed in claim 12, wherein the features selected are the wavelet energies obtained from application of the wavelet transform.

14. A computer program stored on a computer-readable medium for detecting and identifying failure of a valve in a reciprocating compressor, the computer program comprising:

a code segment executable by the computer for monitoring a pressure signal produced by the valve of the reciprocating compressor;

a code segment executable by the computer for monitoring a temperature signal produced by the valve of the reciprocating compressor;

a code segment executable by the computer for applying a time-frequency analysis to the pressure signal so as to obtain a pressure waveform;

a code segment executable by the computer for applying a wavelet transform to the pressure waveform so as to obtain a plurality of features;

a code segment executable by the computer for inputting the plurality of features into a logistic regression model; and

a code segment executable by the computer for obtaining from the logistic regression model a probability of valve failure.

15. The computer program as claimed in claim 14, wherein the pressure and temperature signals are monitored using at least one sensor operably connected with the reciprocating compressor and the computer.

16. The computer program as claimed in claim 14, wherein the logistic regression model is defined as:

17. The computer program as claimed in claim 14, wherein an efficacy of identifying failure of the valve was within the range of approximately 90%-99%.