Transmission failure prediction by leveraging a production impact evaluation metric

1 . A system for managing a maintenance schedule of an operational transmission, the system comprising:

a reciprocating fracturing pump fluidically connected to a wellbore at a wellsite;

the transmission having a plurality of planetary gears and being mechanically coupled at a first end to a power source of the reciprocating fracturing pump and mechanically coupled at a second end to a crankshaft of the reciprocating fracturing pump;

the transmission further being configured to transfer a power output from the power source to the reciprocating fracturing pump and the reciprocating fracturing pump configured to deliver a high-pressure fracturing fluid down the borehole;

a plurality of accelerometers coupled to the transmission, each accelerometer of the plurality of accelerometers being configured to record a plurality of spectra;

a data acquisition subsystem communicatively coupled to each accelerometer of the plurality of accelerometers, and further coupled to a data storage subsystem; and

a transmission maintenance scheduler of the reciprocating fracturing pump comprising a processor and a non-transitory memory, configured to:

collect, using the data acquisition subsystem, each spectrum of the plurality of spectra at a fixed time interval, each spectrum having a timestamp;

store the collected plurality of spectra in the data storage subsystem;

generate, using a spectra processor, from each spectrum of the plurality of spectra (a) statistical features of each spectrum and (b) characteristic frequency (CF) features of each spectrum;

using a timestamp filter, remove the statistical features and the CF features of each target spectrum of the plurality of spectra, each target spectrum having a timestamp corresponding to an idle time of the fracturing pump, and retain a plurality of processed features;

create, using a feature combiner module, a plurality of joined features by joining the plurality of processed features with a plurality of time domain (TD) features;

input the plurality of joined features into an anomaly detection model (ADM), the ADM configured to identify at least one outlier data point in the plurality of joined features, the identified at least one outlier data point indicating a probability of a failure of the transmission;

receive a result from the ADM, the result comprising the probability of the failure of the transmission;

post-process, using a failure alert generator comprising a schedule generator and a schedule optimizer, the result to generate a failure alert; and

based on the generated failure alert, schedule, using the schedule generator, a remediation procedure.

2 . The system of claim 1 , wherein the schedule optimizer is further configured to:

rank the generated failure alert to produce a production impact score, the production impact score being based on a prediction of how much of a usable production time at the wellsite is lost during the remediation procedure, the usable production time being an amount of time when the wellsite is producing an oil, a gas, or another product in a paying quantity;

set a failure threshold, the failure threshold being a predefined threshold probability of an anomaly being present and the anomaly causing the failure of the transmission;

determine that a comparison of the production impact score and the failure threshold predicts that scheduling a remediation procedure increases the usable production time at the wellsite compared to an impact on the usable production time at the wellsite from the transmission experiencing a catastrophic failure; and

confirm a date and a time of the scheduled remediation procedure, the remediation procedure comprising, in response to the comparison of the production impact score and the failure threshold, at least one of: changing an operating condition of the transmission, conducting a general repair of the transmission, or replacing the transmission.

3 . The system of claim 1 , wherein the ADM is a type of machine learning model selected to enhance an accuracy of the probability of failure of the transmission based on a deployment environment of the transmission, and wherein the failure alert of the transmission is displayed on a graphical display interface.

4 . The system of claim 1 , wherein the power source is an electric motor.

5 . The system of claim 1 , wherein the power source is a combustion engine.

6 . The system of claim 1 , wherein the plurality of accelerometers comprises a first accelerometer, a second accelerometer, a third accelerometer, a fourth accelerometer, and a fifth accelerometer.

7 . The system of claim 6 , wherein the first accelerometer and the second accelerometer are configured to record at least one spectrum of the plurality of spectra in a horizontal direction.

8 . The system of claim 7 , wherein the third accelerometer and the fourth accelerometer are configured to record at least one spectrum of the plurality of spectra in a vertical direction.

9 . The system of claim 8 , wherein the fifth accelerometer is configured to record at least one spectrum of the plurality of spectra in an axial direction.

10 . The system of claim 9 , wherein the plurality of planetary gears comprises a first planetary gear, a second planetary gear, a third planetary gear, and a fourth planetary gear.

11 . The system of claim 1 , wherein the fixed time interval is one second.

12 . The system of claim 1 , wherein the statistical features are associated with a measured state of the transmission.

13 . The system of claim 12 , wherein the CF features are associated with a likelihood of a presence of CFs, the CFs being generated by a meshing of the plurality of planetary gears and indicating a likelihood of failure of the transmission.

14 . The system of claim 1 , wherein the statistical features of each spectrum further comprise a mean, a median, a variance, a standard deviation, a kurtosis, a skewness, a root mean square (RMS) value, a crest factor, a frequency center, and a root mean square (RMS) frequency.

15 . The system of claim 1 , wherein post-processing the result comprises generating the failure alert based on a number of times each joined feature in the plurality of joined features exceeds a predefined threshold of anomaly probability associated with each joined feature.

16 . The system of claim 1 , wherein the ADM is a type of machine learning model selected to enhance an accuracy of the probability of failure of the transmission based on a deployment environment of the transmission.

17 . The system of claim 1 , wherein the schedule optimizer is configured to rank the generated failure alert to produce a production impact score, wherein the production impact score is based on a prediction of how much of a usable production time at the wellsite is lost during the remediation procedure, and wherein the usable production time is an amount of time when the wellsite is producing an oil, a gas, or another product in a paying quantity.

18 . The system of claim 17 , wherein the schedule optimizer is further configured to set a failure threshold, wherein the failure threshold is a predefined or a tunable threshold probability of an anomaly being present and the anomaly causing the failure of the transmission.

19 . The system of claim 18 , wherein the schedule optimizer is further configured to determine that a comparison of the production impact score and the failure threshold predicts that scheduling the remediation procedure increases the usable production time at the wellsite compared to an impact on the usable production time at the wellsite from the transmission experiencing a catastrophic failure.

20 . The system of claim 1 , wherein the statistical features are associated with a measured state of the transmission, and wherein the CF features are associated with a likelihood of a presence of CFs, the CFs being generated by a meshing of the plurality of planetary gears and indicating a likelihood of failure of the transmission.