Monitoring machine health using multiple sensors

A non-transitory computer readable medium comprising program code that is executable by a processing device for causing the processing device to:

• receive sensor measurements from a plurality of sensors that detect characteristics of the machine, the sensor measurements being usable as data points to form respective time series associated with respective sensors of the plurality of sensors;
• determine a target sensor to monitor from among the plurality of sensors for detecting an anomaly with the machine;
• determine magnitude values for a particular component of a time series associated with the target sensor by decomposing the time series into a plurality of components;
• generate a dataset comprising the magnitude values for the particular component of the time series and the sensor measurements from the plurality of sensors;
• generate a model using the dataset by assigning respective weights to each respective sensor of the plurality of sensors indicating how the sensor measurements from the respective sensor contribute to the magnitude values for the particular component of the time series associated with the target sensor, the model being representative of a relationship between (i) the sensor measurements from the plurality of sensors, and (ii) the magnitude values for the particular component of the time series associated with the target sensor;
• receive additional sensor measurements from a subset of the plurality of sensors that excludes the target sensor;
• use the model to determine a predicted magnitude value for the particular component of the time series associated with the target sensor based on the additional sensor measurements;
• identify the anomaly with the machine by determining that (i) the predicted magnitude value of the particular component meets or exceeds a predetermined threshold;
• or (ii) multiple predicted magnitude values for the particular component comprise a predetermined pattern that is indicative of the anomaly;
• and
• based on identifying the anomaly, output a notification indicative of the anomaly.

A method for performing real-time monitoring of a machine, the method comprising:

• receiving sensor measurements from a plurality of sensors that detect characteristics of the machine, the sensor measurements being usable as data points to form respective time series associated with respective sensors of the plurality of sensors;
• determining a target sensor to monitor from among the plurality of sensors for detecting an anomaly with the machine;
• determining, by a processor, magnitude values for a particular component of a time series associated with the target sensor by decomposing the time series into a plurality of components;
• generating, by the processor, a dataset comprising the magnitude values for the particular component of the time series and the sensor measurements from the plurality of sensors;
• generating, by the processor, a model using the dataset by assigning respective weights to each respective sensor of the plurality of sensors indicating how the sensor measurements from the respective sensor contribute to the magnitude values for the particular component of the time series associated with the target sensor, the model being representative of a relationship between (i) the sensor measurements from the plurality of sensors, and (ii) the magnitude values for the particular component of the time series associated with the target sensor;
• receiving, by the processor, additional sensor measurements from a subset of the plurality of sensors that excludes the target sensor;
• using, by the processor, the model to determine a predicted magnitude value for the particular component of the time series associated with the target sensor based on the additional sensor measurements;
• identifying, by the processor, the anomaly with the machine by determining that (i) the predicted magnitude value of the particular component meets or exceeds a predetermined threshold;
• or (ii) multiple predicted magnitude values for the particular component comprise a predetermined pattern that is indicative of the anomaly;
• and
• based on identifying the anomaly, outputting, a notification indicative of the anomaly.