Detection of deviation from an operating state of a device

Abstract

A method of detecting deviation from an operational state of a device includes obtaining preprocessed data corresponding to data sensed by one or more sensor devices coupled to the device, where obtaining the preprocessed data includes applying a transform to the data sensed by the one or more sensor devices to generate a set of features in a frequency domain. The method also includes processing the preprocessed data using a trained anomaly detection model to generate an anomaly score. The method also includes processing the anomaly score using an alert generation model to determine whether to generate an alert.

What is claimed is: 
     
         1 . A method of detecting deviation from an operational state of a device, the method comprising:
 obtaining preprocessed data corresponding to data sensed by one or more sensor devices coupled to the device, wherein obtaining the preprocessed data includes applying a transform to the data sensed by the one or more sensor devices to generate a set of features in a frequency domain;   processing the preprocessed data using a trained anomaly detection model to generate an anomaly score; and   processing the anomaly score using an alert generation model to determine whether to generate an alert.   
     
     
         2 . The method of  claim 1 , wherein processing the preprocessed data at the trained anomaly detection model includes:
 inputting the preprocessed data to an autoencoder; and   generating a residual based on an output of the autoencoder, wherein the anomaly score is generated based on the residual.   
     
     
         3 . The method of  claim 2 , wherein processing the preprocessed data at the trained anomaly detection model further includes:
 inputting the residual to a Hotelling test statistics module; and   generating the anomaly score at the Hotelling test statistics module using a multivariate test statistic that is based on residual data and reference residual data.   
     
     
         4 . The method of  claim 2 , further comprising, prior to obtaining the preprocessed data:
 receiving a first time series of data indicative of normal operation of the device from the one or more sensor devices; and   training the autoencoder based on the first time series.   
     
     
         5 . The method of  claim 1 , wherein the alert generation model includes a sequential probability ratio test that determines whether a set of one or more anomaly scores indicates deviation from normal operation of the device. 
     
     
         6 . The method of  claim 1 , wherein the data sensed by the one or more sensor devices indicates at least one of a motion or an acceleration associated with vibration of the device. 
     
     
         7 . The method of  claim 1 , further comprising determining a contribution of each feature to the anomaly score. 
     
     
         8 . The method of  claim 1 , further comprising generating a graphical user interface including:
 a graph indicative of a performance metric of the device over time;   an alert indication corresponding to a portion of the graph; and   an indication of one or more sets of feature data associated with the alert indication.   
     
     
         9 . A system to detect deviation from an operational state of a device, the system comprising:
 a memory including a trained anomaly detection model and an alert generation model; and   one or more processors coupled to the memory, the one or more processors configured to:
 obtain data sensed by one or more sensor devices coupled to the device; 
 apply a transform to the data sensed by the one or more sensor devices to generate preprocessed data including a set of features in a frequency domain; 
 process the preprocessed data using the trained anomaly detection model to generate an anomaly score; and 
 process the anomaly score using the alert generation model to determine whether to generate an alert. 
   
     
     
         10 . The system of  claim 9 , wherein the trained anomaly detection model includes:
 an autoencoder configured to generate a reconstruction of the preprocessed data; and   a residual generator configured to generate a residual based on an output of the autoencoder, wherein the anomaly score is generated based on the residual.   
     
     
         11 . The system of  claim 10 , wherein the trained anomaly detection model further includes a Hotelling test statistics module configured to generate the anomaly score based on the residual. 
     
     
         12 . The system of  claim 10 , wherein the memory further includes a calibration module that is executable by the one or more processors to:
 receive a first time series of data indicative of normal operation of the device from the one or more sensor devices; and   train the autoencoder based on the first time series.   
     
     
         13 . The system of  claim 9 , wherein the alert generation model includes a sequential probability ratio test that determines whether a set of one or more anomaly scores indicates deviation from normal operation of the device. 
     
     
         14 . The system of  claim 9 , wherein the data sensed by the one or more sensor devices indicates at least one of a motion or an acceleration associated with vibration of the device. 
     
     
         15 . The system of  claim 9 , wherein the one or more processors are configured to determine a contribution of each feature to the anomaly score. 
     
     
         16 . The system of  claim 9 , wherein the memory further includes a graphical user interface module that is executable by the one or more processors to generate a graphical user interface to display an alert indication. 
     
     
         17 . A computer-readable storage device storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations to detect deviation from an operational state of a device, the operations comprising:
 obtaining preprocessed data corresponding to data sensed by one or more sensor devices coupled to the device, wherein obtaining the preprocessed data includes applying a transform to the data sensed by the one or more sensor devices to generate a set of features in a frequency domain;   processing the preprocessed data using a trained anomaly detection model to generate an anomaly score; and   processing the anomaly score using an alert generation model to determine whether to generate an alert.   
     
     
         18 . The computer-readable storage device of  claim 17 , wherein processing the preprocessed data at the trained anomaly detection model includes:
 inputting the preprocessed data to an autoencoder; and   generating a residual based on an output of the autoencoder, wherein the anomaly score is generated based on the residual.   
     
     
         19 . The computer-readable storage device of  claim 18 , wherein processing the preprocessed data at the trained anomaly detection model further includes:
 inputting the residual to a Hotelling test statistics module; and   generating the anomaly score at the Hotelling test statistics module using a multivariate test statistic that is based on residual data and reference residual data.   
     
     
         20 . The computer-readable storage device of  claim 17 , wherein the alert generation model includes a sequential probability ratio test that determines whether a set of one or more anomaly scores indicates deviation from normal operation of the device.