Machine learning based predictive maintenance of equipment

A computer-implemented method comprising:

• receiving a set of sensor data generated from sensors associated with equipment, one of the sensors being a target sensor, the set of sensor data comprising measured values of the target sensor;
• selecting a subset of sensor data, the subset of sensor data comprising data generated from the sensors and excluding the measured values of the target sensor;
• inputting the subset of sensor data into a machine learning model to generate predicted sensor values of the target sensor;
• determining differences between the predicted sensor values of the target sensor and the measured values of the target sensor, wherein training of the machine learning model comprises:
• receiving a set of training sensor data comprising historical data of the sensors, the set of training sensor data comprising historical measured values of the target sensor,
• determining a subset of training sensor data comprising the historical data generated from the sensors excluding the target sensor,
• inputting the subset of training sensor data to the machine learning model to generate outputs of the machine learning models, and
• adjusting weights of the machine learning models to reduce training errors of the outputs compared to the historical measured values of the target sensor;
• generating an anomaly score for the equipment based on the differences;
• and
• generating, based on anomaly score, an alert for the equipment.

A non-transitory computer-readable medium configured to store computer code comprising instructions, the instructions, when executed by a processor, cause the processor to perform steps comprising:

• receiving a set of sensor data generated from sensors associated with equipment, one of the sensors being a target sensor, the set of sensor data comprising measured values of the target sensor;
• selecting a subset of sensor data, the subset of sensor data comprising data generated from the sensors and excluding the measured values of the target sensor;
• inputting the subset of sensor data into a machine learning model to generate predicted sensor values of the target sensor;
• determining differences between the predicted sensor values of the target sensor and the measured values of the target sensor;
• normalizing the differences using a reference metric of the machine learning model, the reference metric corresponding to training error of the machine learning model in predicting values of the target sensor;
• generating an anomaly score for the equipment based on the differences;
• and
• generating, based on anomaly score, an alert for the equipment.