Longitudinal failure analysis using obtained sensor data of a physical machine

A physical equipment monitoring system, comprising:

• multiple sensor devices, each attached to separate physical equipment, wherein each of the multiple sensor devices comprise an accelerometer and a thermal sensor, and wherein each attached sensor device is configured to periodically generate data and transmit the generated data to a computing device, and wherein the generated data includes a timestamp for each respective occurrences of the generated data, and wherein the sensor device is configured to receive remote configuration files and/or command to change operational functionality of the sensor device;
• and
• one or more servers, comprising one or more non-transitory computer-readable media storing computer-executable instructions that, when executed on one or more processors, cause the one or more processors to perform operations:
• receiving, via the one or more servers, from each of the multiple sensor devices, data generated by the attached sensor devices;
• storing the generated data in a data repository, wherein the data repository includes generated data obtained from the multiple different sensors;
• determining, via the one or more servers, based on periodically received generated data for a first sensor device of the of the multiple sensor devices, the occurrence of one or more anomalies associate with a first physical equipment being monitored by the first sensor device;
• determining, via the one or more servers, a condition state associated with the first physical equipment, wherein the condition state is determined by one or more trained machine learning models that receive as an input, at least a portion of the periodically received generated data generated by the first sensor device;
• generating, via the one or more servers, a first user interface depicting the determined one or more anomalies and the condition state, and depicting detailed information associated with an input selection, via the first user interface, of one of the one or more anomalies;
• and
• based on a change of the determined condition state, generating a configuration file and/or a command for transmission to the first sensor device;
• wherein the first sensor device is configured to perform the operations of:
• receiving by the first sensor device, the generated configuration file and/or command;
• based on the received generated configuration file and/or command, changing a sampling parameter of the first sensor device to control the operation of the first sensor device operation, wherein the sampling parameter includes any one of a sampling interval, a sampling frequency, a sampling rate, a sampling range or a sampling resolution;
• and
• operating the first sensor device according to the changed sampling parameter;
• wherein the one or more processors further perform the operations of:
• periodically receiving, by the one or more servers, additional data generated by the first sensor device;
• evaluating, at least in part, the additional data generated by the first sensor device, to determine that the condition state associated with the first physical equipment has changed to a different condition state, wherein the evaluation is performed by the one or more trained machine learning model that receive as an input the additional data generated by the first sensor device;
• and
• generating a second user interface depicting the determined one or more anomalies, and depicting information indicative of the different condition state.