US-20260126489-A1 - COMPUTER-IMPLEMENTED METHOD FOR PREDICTING A BROKEN BAR DEFECT IN A THREE-PHASE MOTOR

US20260126489A1US 20260126489 A1US20260126489 A1US 20260126489A1US-20260126489-A1

Abstract

The proposed technique consists of a computer-implemented method for predicting broken bar failures in three-phase motors based on artificial intelligence. Motor vibration, ambient temperature and humidity, and current parameters in each phase are used as inputs in a previously trained Artificial Intelligence (AI) to determine the probability of broken bar failure in each phase. Furthermore, an FFT is performed on each current to determine its fundamental frequency and perform windowing to the left and right of the fundamental frequency, extracting parameters such as mean value, standard deviation, maximum value, minimum value, kurtosis, skewness, and spacing from each window. These parameters are also used as inputs in the previously trained AI.

Inventors

MARCOS LEONARDO RAMOS
Marlon Moratti do Amaral
Alberto Ferreira De Souza
Daniel Carletti
LUCAS FRIZERA ENCARNAÇÃO
Waldemar Junior TOZI
Abrahao Da Silva FONTES
Claudine Santos BADUE
Thiago Oliveira Dos Santos
Ronimar Espindula VOLKERS
Ramon Pereira DOS SANTOS
Gabriel Braga Ladislau

Assignees

Petróleo Brasileiro S.A. - Petrobras
UNIVERSIDADE FEDERAL DO ESPÍRITO SANTO - UFES
SEVEN SCIENCE SYSTEMS LTDA

Dates

Publication Date: 20260507
Application Date: 20251029
Priority Date: 20241105

Claims (10)

1 . A computer-implemented method for predicting a broken bar defect in a three-phase motor, comprising: obtaining current and voltage data for each phase of the three-phase motor; performing Fast Fourier Transform (FFT) for each current and then flattening the FFT; performing a three-point windowing to a left and a right centered on a fundamental frequency; for each window, calculating a mean value, a standard deviation, a maximum value, a minimum value, a kurtosis, a skewness, and a spacing; using the calculated mean value, standard deviation, maximum value, minimum value, kurtosis, skewness, and spacing as input data in a previously trained Artificial Intelligence; and determining, using the previously trained Artificial Intelligence, a probability of a broken bar failure in each of the phases of the three-phase motor.
2 . The method of claim 1 , further comprising obtaining vibration data for the three-phase motor.
3 . The method of claim 2 , wherein the calculated mean value, standard deviation, maximum value, minimum value, kurtosis, skewness, and spacing are used as the input data in the previously trained Artificial Intelligence combined with the obtained vibration data.
4 . The method of claim 1 , further comprising obtaining humidity and temperature data for the environment where the three-phase motor is located.
5 . The method of claim 4 , wherein the calculated mean value, standard deviation, maximum value, minimum value, kurtosis, skewness, and spacing are used as the input data in the previously trained Artificial Intelligence combined with the obtained humidity and temperature data.
6 . The method of claim 1 , wherein the calculated mean value, standard deviation, maximum value, minimum value, kurtosis, skewness, and spacing are used as the input data in the previously trained Artificial Intelligence combined with the obtained current and voltage data.
7 . The method of claim 1 , further comprising determining the fundamental frequency of each current based on the FFT for each current.
8 . The method of claim 1 , wherein the determining of the probability of the broken bar failure comprises determining, separately for each phase, a probability of no failure, a probability of a low-severity failure, a probability of a medium-severity failure, and a probability of a high-severity failure.
9 . A computer-implemented method for predicting a broken bar defect in a three-phase motor, comprising: obtaining current and voltage data for each phase of the three-phase motor; obtaining vibration data for the three-phase motor; obtaining humidity and temperature data for the environment where the three-phase motor is located; performing Fast Fourier Transform (FFT) for each current and then flattening the FFT; determining a fundamental frequency of each current based on the FFT for each current; performing a three-point windowing to a left and a right centered on the fundamental frequency; for each window, calculating a mean value, a standard deviation, a maximum value, a minimum value, a kurtosis, a skewness, and a spacing; using the calculated mean value, standard deviation, maximum value, minimum value, kurtosis, skewness, and spacing as input data in a previously trained Artificial Intelligence, combined with the obtained current and voltage data, the obtained vibration data, and the obtained humidity and temperature data; and determining, using the previously trained Artificial Intelligence, a probability of a broken bar failure in each of the phases of the three-phase motor.
10 . The method of claim 9 , wherein the determining of the probability of the broken bar failure comprises determining, separately for each phase, a probability of no failure, a probability of a low-severity failure, a probability of a medium-severity failure, and a probability of a high-severity failure.

Description

CROSS REFERENCE TO RELATED APPLICATIONS The present application claims priority to BR patent application Ser. No. 1020240230523 filed on Nov. 5, 2024, which is hereby incorporated by reference in its entirety. FIELD OF THE INVENTION The present invention relates to electrical engineering. More specifically, the present invention describes an artificial intelligence-based method for automatically and objectively predicting the probability of a broken bar failure in a three-phase motor. BACKGROUND OF THE INVENTION Modern industrial activity currently depends on energy-efficient and operationally efficient equipment. Generally speaking, electric motors represent a significant portion of the equipment responsible for producing mechanical energy for a wide range of industrial processes. Despite being robust, they can show defects inherent to their operation, even when a comprehensive maintenance plan is strictly followed. Defects that can cause industrial process shutdowns are not always easily overcome with an appropriate maintenance plan. To circumvent this problem and avoid unscheduled downtime, prescriptive diagnostics are usually employed. This involves monitoring and analyzing the operating conditions of the equipment and electrical and physical signals to assess the presence of a fault or predict the possibility of a future fault. Even with the most modern fault analysis equipment available on the market, it is still necessary to rely on the expertise of the technician who interprets these signals. However, this process is costly in both time and money. STATE OF THE ART The document CN 117928642 A, entitled “State Monitoring and Fault Diagnosis System for Motor Rolling Bearings”, describes a condition monitoring and fault diagnosis system for motor bearings, comprising a data acquisition side, a server side, and a data display side. The data acquisition side is used to collect data from multiple measurement points on the motor bearing according to a predefined data sampling frequency to obtain a raw data set and monitor data from video. The server side is used to receive the original data set sent by the data acquisition side and perform data preprocessing and analysis on the original data set to obtain a set of important data information. The server side is also used to determine the operating status of the motor bearing using this set of important data information and to perform fault analysis on the abnormally operating motor bearing to determine the type of fault. The document CN 117269752 A, titled “An Operating Status Monitoring Device and Method Suitable for Three-Phase High-Voltage Asynchronous Motors”, discloses an operating status monitoring device suitable for a three-phase high-voltage asynchronous motor, including a switching power supply module to supply power to the operating status monitoring device; a three-phase voltage sampling circuit to collect real-time values of the three-phase voltage; and a current sampling circuit. The three-phase current sampling circuit uses an external high-precision open-type current sensor to collect real-time values of three-phase currents. A high-speed sampling module collects real-time values of three-phase voltages and currents. The values are converted into digital signals. A processor module connected to the high-speed sampling module analyzes the operating status of the high-voltage three-phase asynchronous motor based on the received digital signal and determines whether the high-voltage three-phase asynchronous motor has a fault and the severity of the fault. The analysis results are transmitted through the communication module. The data storage module is used for recording timing waves, recording fault waves, and storing fixed values and parameters. SUMMARY OF THE INVENTION The present invention aims to provide a computer-implemented method capable of analyzing the operating status of an electric motor, diagnosing possible defects and/or imminent failures, and producing comprehensive reports with suggestions for corrective and mitigating actions to postpone a failure until it is possible to schedule a maintenance shutdown. The invention aims to eliminate subjectivity in the diagnosis of electric motor failures caused by technicians. The computer-implemented method described here adds value to the production chain of processes involving motors by prescriptively detecting defects and their severity. This improves fault analysis and diagnosis, supports decision-making, and empowers field teams with rapid and accurate diagnosis, significantly reducing the costs of corrective and predictive maintenance and equipment acquisition. The unique feature of this invention is the implementation of machine learning and artificial intelligence to complement the current signature technique for fault analysis, fault prediction, and prescription. Also included is the module for Classifying Rotor Broken Bars of Induction Motors with Synthetic Data, an approach that l