System and method for turbomachinery preventive maintenance and root cause failure determination
First Claim
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1. A method for determining root causes of failure in electrically powered turbomachinery, comprising:
- a) sensing physical parameters, in the course of operation of the machine, including at least one of motor speed, vibration, magnetic field presence and strength, temperature, relative humidity, infrared radiation, input voltage, input current and phase characteristics of input power;
b) providing time domain output data indicative of the sensed physical parameters to first and second parallel processing blocks;
c) processing the time domain output data in the second processing block by;
i) selecting a parameter data sample;
ii) transforming the data sample into the frequency domain by performing Fourier transformation thereon;
iii) extracting metadata for selected characteristics of the sensed physical parameters from the transformed data;
d) continuously monitoring the time domain data and performing trend analysis thereon for selected parameter characteristics; and
e) upon detecting deviation of a selected characteristic in the time domain data from a preselected value, sampling the frequency domain metadata for a preselected time interval and then analyzing the frequency domain selected parameter characteristic according to a preselected algorithm relative to a base value to detect as a root cause of failure in the turbomachine any variation of the parameter from a base value.
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Abstract
Predicting maintenance needs and analyzing preventative maintenance requirements in electrically powered turbomachinery with multi-parameter sensors and power quality sensors, both of the Fog-type, providing time domain output data and transforming data samples into the frequency domain to detect a root cause of failure of the machinery.
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Citations
10 Claims
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1. A method for determining root causes of failure in electrically powered turbomachinery, comprising:
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a) sensing physical parameters, in the course of operation of the machine, including at least one of motor speed, vibration, magnetic field presence and strength, temperature, relative humidity, infrared radiation, input voltage, input current and phase characteristics of input power; b) providing time domain output data indicative of the sensed physical parameters to first and second parallel processing blocks; c) processing the time domain output data in the second processing block by; i) selecting a parameter data sample; ii) transforming the data sample into the frequency domain by performing Fourier transformation thereon; iii) extracting metadata for selected characteristics of the sensed physical parameters from the transformed data; d) continuously monitoring the time domain data and performing trend analysis thereon for selected parameter characteristics; and e) upon detecting deviation of a selected characteristic in the time domain data from a preselected value, sampling the frequency domain metadata for a preselected time interval and then analyzing the frequency domain selected parameter characteristic according to a preselected algorithm relative to a base value to detect as a root cause of failure in the turbomachine any variation of the parameter from a base value.
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2. A system for determining root causes of failure in electrically powered turbomachinery, comprising:
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a) sensor device connected to an electric motor powering the turbomachinery, for sensing physical parameters including at least one of motor speed, vibration, magnetic field presence and strength, temperature, relative humidity, infrared radiation, input voltage, input current and phase characteristics of input power, and providing output data indicative of the sensed physical parameters; b) a communication network connected to the device for receiving the output data therefrom; c) a first computing device connected to the communication network for receiving the output data therefrom, having first and second parallel processing blocks each receiving time domain sensed data from the network, the second processing block processing the time domain data by; i) selecting a sensed physical parameter data sample size and duration to be within the processing capacity of the first computing device; ii) transforming the data into the frequency domain by performing Fourier transformation thereon; iii) extracting metadata for selected characteristic(s) of the sensed parameter(s) from the transformed data; d) an edge cloud computing device connected to the first computing device by the communication network, receiving from the first processing block time domain data for analysis thereof and receiving from the second processing block frequency domain metadata extracted from the transformed data, for; i) continuously monitoring and performing trend analysis on the time domain data for selected parameter characteristics; ii) upon detecting deviation of a selected characteristic in the time domain data from a preselected value, sampling the frequency domain metadata for a preselected time interval and then analyzing the selected parameter characteristic according to preselected algorithm relative to a base value to detect as a root cause of failure in the turbomachinery any variation of the parameter from the base value.
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3. A system identifying root causes of vibration induced failures in electrically powered turbo machinery, comprising:
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a) a vibration sensor connected to an electric motor powering the turbomachinery and providing time domain output data indicative of sensed vibration parameters; b) a communication network connected to the sensor for receiving the output data therefrom; c) a first computing device connected to the communication network for receiving the output data therefrom, having first and second parallel processing blocks each receiving the time domain data from the network, the second processing block processing the time domain data by; i) selecting a data sample of preselected size and duration; ii) transforming the data into the frequency domain by performing Fourier transformation thereon; iii) extracting metadata for selected vibration parameters from transformed data; d) an edge cloud computing device connected to the first computing device by the communication network, receiving from the first processing block time domain data for analysis thereof and receiving from the second processing block frequency domain metadata extracted from the transformed data, for; i) continuously monitoring and performing trend analysis on the time domain data for selected characteristics indicative of status of the vibration parameters; ii) upon detecting variance of the selected characteristic in the time domain data from a preselected value, sampling the frequency domain metadata for a preselected time interval and analyzing the sampled frequency domain metadata for the selected characteristics according to a preselected algorithms relative to base values of the characteristics to detect as a root cause of a vibration induced turbomachinery failure any deviations of the characteristics from the base values.
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4. In a system for determining root causes of failure in electrically powered turbomachinery, having a sensor device connected to an electric motor powering the turbomachinery, for sensing at least one physical parameter, and providing output data indicative of the sensed physical parameter, and a communication network connected to the device for receiving the output data therefrom, the improvement consisting of:
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a) a first computing device connected to the communication network for receiving the output data therefrom, having parallel processing blocks each receiving time domain sensed data from the network, one of the processing blocks processing the time domain data by; i) selecting a sample size and duration to be within the processing capacity of the first computing device; ii) transforming the data into the frequency domain by performing Fourier transformation thereon; iii) extracting metadata for selected characteristics of the sensed parameter from the transformed data; and b) an edge cloud computing device connected to the first computing device by the communication network, receiving from the first processing block time domain data for analysis thereof and receiving from the second processing block frequency domain metadata extracted from the transformed data, for; i) continuously monitoring and performing trend analysis on the time domain data for at least one characteristic of the physical parameter; ii) upon detecting deviation of the selected characteristic in the time domain data from a preselected value, sampling the frequency domain metadata for a preselected time interval and then analyzing a the selected parameter characteristic according to a preselected algorithm relative to a base value to detect as a root cause of failure in the turbomachinery any variation of the parameter from a base value.
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5. A method for predicting maintenance needs, analyzing preventative maintenance e requirements and determining toot causes of failure in electrically powered turbomachinery, comprising:
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a) mounting a multi-parameter sensor, of the Fog-type having micro-computer component, on the turbomachine; b) attaching a power quality sensor, of the Fog-type having a micro-computer component, to the electrical line providing electrical power to the turbomachine; c) transmitting data from the multi-parameter sensor and date from the power quality sensor in the time domain to a computing device which is connected to the cloud via the Internet; d) providing time domain output data indicative of the sensed physical parameters to first and second parallel processing blocks; e) processing the time domain output data in the second processing block by; i) selecting sensed physical parameter time domain data samples; ii) transforming the selected data samples into the frequency domain by performing Fourier transformation thereon; iii) extracting metadata for selected characteristics of the sensed physical parameters from the transformed data samples; f) continuously monitoring the time domain output data and performing trend analysis thereon for selected characteristics of the sensed physical parameters; and g) upon detecting deviation of a selected characteristic in the time domain data from a preselected value, sampling the frequency domain metadata for the selected characteristic for a preselected time interval and then analyzing the frequency domain selected parameter characteristic data according to a preselected algorithm relative to a base value to detect as a root cause of failure in the turbomachine any variation of the parameter from the base value. - View Dependent Claims (6, 7, 8, 9, 10)
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Specification