Method for predictive maintenance through automatic prediction of pump anomaly

A method for predictive maintenance through automatic prediction of a pump anomaly, performed by a computing system, the method comprising:

• receiving a plurality of sensing values from a plurality of sensors provided in a pump, the plurality of sensing values including a sensing value of a Body Power (BP), a sensing value of a Dry Power (DP), a sensing value of a piping pressure, and a sensing value of a body temperature;
• inputting a first feature representing the sensing value of the BP, a second feature representing the sensing value of the DP, a third feature representing the sensing value of the piping pressure, and a fourth feature representing the sensing value of the body temperature, to an anomaly prediction model that is machine-learned in advance;
• determining whether a future anomaly of the pump is predicted to occur, by using data output from the anomaly prediction model;
• and
• providing alarm information based on a determination that the future anomaly is predicted to occur,
• wherein, among a plurality of pump model groups including a first pump model group and a second pump model group, the pump belongs to the second pump model group matched with the anomaly prediction model.

A method for predictive maintenance through automatic prediction of a pump anomaly, performed by a computing system, the method comprising:

• receiving a plurality of sensing values from a plurality of sensors provided in a pump, the plurality of sensing values including a sensing value of a voltage, a sensing value of a temperature, and a sensing value of a piping pressure;
• inputting a first feature representing the sensing value of the voltage, a second feature representing the sensing value of the temperature, and a third feature representing the sensing value of the piping pressure to an anomaly prediction model that is machine-learned in advance;
• determining whether a future anomaly of the pump is predicted to occur, by using data output from the anomaly prediction model;
• and
• providing alarm information based on a determination that the future anomaly is predicted to occur, and
• wherein, among a plurality of pump model groups including a first pump model group to a third pump model group, the pump belongs to the third pump model group matched with the anomaly prediction model.

A method for predictive maintenance through automatic prediction of a pump anomaly, performed by a computing system, the method comprising:

• receiving a plurality of sensing values from a plurality of sensors provided in a pump, the plurality of sensing values including a sensing value of each of a Body Power (BP), a sensing value of a Dry Power (DP), a sensing value of a body temperature, a sensing value of a body voltage, a sensing value of a dry voltage, and a sensing value of a piping temperature;
• inputting a first feature representing the sensing value of the BP, a second feature representing the sensing value of the DP, a third feature representing the sensing value of the body temperature, a fourth feature representing the sensing value of the body voltage, a fifth feature representing the sensing value of the dry voltage, and a sixth feature representing the sensing value of the piping temperature to an anomaly prediction model that is machine-learned in advance;
• determining whether a future anomaly of the pump is predicted to occur, by using data output from the anomaly prediction model;
• and
• providing alarm information based on a determination that the future anomaly is predicted to occur, and
• wherein, among a plurality of pump model groups including a first pump model group to a fourth pump model group, the pump belongs to the fourth pump model group matched with the anomaly prediction model.