Systems, methods, and media for generating alerts of water hammer events in steam pipes
Mechanism, which can include systems, methods, and media, for generating an alert of a water hammer event in a steam pipe are provided, the mechanisms comprising: sampling an accelerometer coupled to a steam pipe to provide accelerometer data; determining that the accelerometer data meets or exceeds a threshold; and generating an alert that a water hammer event has occurred based at least in part on the accelerometer data. In some of the mechanisms, the sampling of the accelerometer is performed for a given period of time, and the mechanisms further comprise sampling an ultrasonic sensor for the given period of time to provide ultrasonic sensor data, and wherein the generating the alert is based at least in part on the accelerometer data and the ultrasonic sensor data.
1. A system for generating an alert of an event in a steam pipe, comprising:
a memory;
a first hardware processor that is coupled to the memory and that is configured to at least:
obtain temperature measurements of the steam trap;
sample an accelerometer coupled to a steam pipe to provide accelerometer data;
determine that the accelerometer data meets or exceeds a threshold;
in response to the determining that the accelerometer data meets or exceeds the threshold:
increase the sample frequency at which the accelerometer is sampled; and
sample an ultrasonic sensor to provide ultrasonic sensor data;
detect a failed closed steam trap by detecting no accelerations in the steam trap, detecting decreased ultrasonic noise based on the ultrasonic sensor data, and detecting a drop in temperature of the steam trap; and
generate an alert of the failed closed steam trap; and
a second hardware processor that is configured to at least:
receive a plurality of alerts that a water hammer event has occurred;
determine that a period of time has passed since a first of the plurality of alerts was received; and
in response to determining that the period of time has passed, compare data from multiple sensor modules to identify one of the multiple sensor modules that is closest to the water hammer event.
2. The system of claim 1 , wherein the sampling of the accelerometer is performed for a given period of time, and wherein the sampling of the ultrasonic sensor is performed for the given period of time.
3. The system of claim 2 , wherein the hardware processor is further configured to perform a Fast Fourier Transform (FFT) operation on the accelerometer data to produce first FFT output data and perform a FFT operation on the ultrasonic sensor data to produce second FFT output data, and wherein the generating the alert is based at least in part on the first FFT output data and the second FFT output data.
4. The system of claim 3 , wherein the hardware processor is further configured to use a machine learning classifier to indicate a likelihood that a water hammer event occurred and wherein the generating the alert is based at least in part an output of the machine learning classifier.
5. The system of claim 4 , wherein the machine learning classifier is a neural network.
6. A method of generating an alert of an event in a steam pipe, comprising:
obtaining temperature measurements of the steam trap;
sampling an accelerometer coupled to a steam pipe to provide accelerometer data;
determining that the accelerometer data meets or exceeds a threshold using a hardware processor;
in response to the determining that the accelerometer data meets or exceeds the threshold:
increasing the sample frequency at which the accelerometer is sampled; and
sampling an ultrasonic sensor to provide ultrasonic sensor data;
detecting a failed closed steam trap by detecting no accelerations in the steam trap, detecting decreased ultrasonic noise based on the ultrasonic sensor data, and detecting a drop in temperature of the steam trap;
generating an alert that of the failed closed steam trap;
receiving a plurality of alerts that a water hammer event has occurred;
determining that a period of time has passed since a first of the plurality of alerts was received; and
in response to determining that the period of time has passed, comparing data from multiple sensor modules to identify one of the multiple sensor modules that is closest to the water hammer event.
7. The method of claim 6 , wherein the sampling of the accelerometer is performed for a given period of time, and wherein the sampling of the ultrasonic sensor is performed for the given period of time.
8. The method of claim 7 , further comprising performing a Fast Fourier Transform (FFT) operation on the accelerometer data to produce first FFT output data and performing a FFT operation on the ultrasonic sensor data to produce second FFT output data, and wherein the generating the alert is based at least in part on the first FFT output data and the second FFT output data.
9. The method of claim 8 , further comprising using a machine learning classifier to indicate a likelihood that a water hammer event occurred and wherein the generating the alert is based at least in part an output of the machine learning classifier.
10. The method of claim 9 , wherein the machine learning classifier is a neural network.
11. A non-transitory computer-readable medium containing computer executable instructions that, when executed by a processor, cause the processor to perform a method for generating an alert of an event in a steam pipe, the method comprising:
obtaining temperature measurements of the steam trap;
sampling an accelerometer coupled to a steam pipe to provide accelerometer data;
determining that the accelerometer data meets or exceeds a threshold;
in response to the determining that the accelerometer data meets or exceeds the threshold:
increasing the sample frequency at which the accelerometer is sampled; and
sampling an ultrasonic sensor to provide ultrasonic sensor data;
detecting a failed closed steam trap by detecting no accelerations in the steam trap, detecting decreased ultrasonic noise based on the ultrasonic sensor data, and detecting a drop in temperature of the steam trap;
generating an alert that of the failed closed steam trap;
receiving a plurality of alerts that a water hammer event has occurred;
determining that a period of time has passed since a first of the plurality of alerts was received; and
in response to determining that the period of time has passed, comparing data from multiple sensor modules to identify one of the multiple sensor modules that is closest to the water hammer event.
12. The non-transitory computer-readable medium of claim 11 , wherein the sampling of the accelerometer is performed for a given period of time, and wherein the sampling of the ultrasonic sensor is performed for the given period of time.
13. The non-transitory computer-readable medium of claim 12 , wherein the method further comprises performing a Fast Fourier Transform (FFT) operation on the accelerometer data to produce first FFT output data and performing a FFT operation on the ultrasonic sensor data to produce second FFT output data, and wherein the generating the alert is based at least in part on the first FFT output data and the second FFT output data.
14. The non-transitory computer-readable medium of claim 13 , wherein the method further comprises using a machine learning classifier to indicate a likelihood that a water hammer event occurred and wherein the generating the alert is based at least in part an output of the machine learning classifier.
15. The non-transitory computer-readable medium of claim 14 , wherein the machine learning classifier is a neural network.