TECHNICAL FIELD

The disclosure relates in general to detecting anomalies in equipment. More specifically, the disclosure relates to using machine learning based techniques to detect anomaly of equipment based on sensor measurements of the equipment.

BACKGROUND

Various industrial or commercial systems include a wide range of equipment that operates in high capacities. When a piece of equipment breaks or otherwise malfunctions, not only it is often costly to repair the equipment but the breakdown could also result in catastrophic effects or chain reactions that affect other components of a system. Conventionally, equipment maintenance often involves only reactive approaches or rule-based approaches. In a reactive approach, operators of the systems repair the equipment when malfunctions occur and detected. In a rule-based approach, operators of the system perform repair or other maintenance when certain conditions are met. However, those conditions often fail to detect any anomaly in a piece of equipment during routine inspections. Problems are often only detected when the situation has become very costly for the operators to remedy.

SUMMARY

In one embodiment, a computer-implemented predictive maintenance process is described. The process may include 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. The process may also include 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. The process may further include inputting the subset of sensor data into a machine learning model to generate predicted sensor values of the target sensor. The process may further include determining differences between the predicted sensor values of the target sensor and the measured values of the target sensor. The process may further include generating an anomaly score for the equipment based on the differences. The process may further include generating, based on anomaly score, an alert for the equipment.

In one embodiment, a computer-implemented predictive maintenance process is described. The process may include receiving a set of sensor data from equipment. The process may also include inputting the set of sensor data into a neural network. The neural network may include a plurality of intermediate layers that comprise a bottleneck layer. The bottleneck layer may have a number of nodes that is smaller than both a number of nodes in a preceding layer and a number of nodes in a succeeding layer. The process may further include generating a distribution using one or more statistical parameters that are generated by the neural network. The process may further include generating a probability density of the set of sensor data using the distribution. The process may further include generating an alert for the equipment based on the probability density.

In one embodiment, a computer-implemented predictive maintenance process is described. The process may include receiving a first set of sensor data sent during a first time interval by a sensor of equipment. The process may also include generating a reference histogram based on the first set of sensor data. The reference histogram may represent a first distribution of sensor data during the first time interval. A histogram may include a plurality of bins. Each bin may represent a range of sensor data values and a number of sensor data points for each bin. The process may further include receiving a second set of sensor data sent during a second time interval by the sensor of the equipment. The process may further include generating a new histogram based on the first set of sensor data, the new histogram representing a second distribution of sensor data during the second time interval. The process may further include comparing the new histogram with the reference histogram. The process may further include determining whether there is an anomaly in the operation of the equipment based on the comparison. The process may further include, responsive to determining that there is an anomaly, generating an alert for the equipment.

In one embodiment, a non-transitory computer readable medium that is configured to store instructions is described. The instructions, when executed by one or more processors, cause the one or more processors to perform a process that includes steps described in the above computer-implemented methods or described in any embodiments of this disclosure. In one embodiment, a system may include one or more processors and a storage medium that is configured to store instructions. The instructions, when executed by one or more processors, cause the one or more processors to perform a process that includes steps described in the above computer-implemented methods or described in any embodiments of this disclosure.

