Amazon Technologies, Inc.

Palo Alto Research Center Inc.

Panasonic Holdings Corporation

Fangzhou Cheng

Ritwik Giri

Karim Helwani

Arvindh Krishnaswamy

Mehmet Umut Isik

Srikanth Venkata Tenneti

Ajay Raghavan

Deokwoo Jung

Aparna Pandey

Tetsuyoshi Ogura

Akira Minegishi

Yosuke Tajika

Yukinori Sasaki

Systems, methods, and apparatuses for selecting a model are described. A method includes receiving a request to perform model selection and evaluating multiple models by generating various metrics for each trained model. These metrics include a forewarning time (how much in advance of a failure an alert can be raised), an event recall metric (how many failure events were alerted to in advance), an event precision metric (ratio of true and false positives, and an area under a receiver operating characteristic (ROC) curve. For each trained model, a weighted harmonic mean is calculated from these metrics. A model is then selected based on the calculated means and a report on the selected model is generated and provided.

Prognostics and health management service

A group masked autoencoder may be implemented for anomaly detection. An autoencoder network model may be trained without supervision and applied to output an estimated joint probability distribution of normality for a group of frames of time series data. The estimated joint probability distribution may be used to determine an anomaly score for the time series data. An anomaly may be detected according to the anomaly score and a result that indicates a detected anomaly may be provided.

Group masked autoencoder for anomaly detection

Systems, methods, and apparatuses for detecting anomalies using clusters are described. In some examples, a method includes receiving a request to perform anomaly detection using a plurality of clusters; receiving a data point; determining when the received data point is a part of one of the plurality of clusters utilizing a distance to centers of the one or more clusters, wherein: when the received data point is determined to belong to a normal cluster, assigning the received data point to the determined cluster, updating the cluster, and updating a history for the cluster, when the received data point is determined to belong to an anomalous cluster, raising an anomaly, updating the cluster, and updating a history for the cluster, and when the received data point is determined to not belong to any cluster, raising an anomaly.

One embodiment can provide a system for detecting anomaly for high-dimensional sensor data associated with one or more machines. During operation, the system can obtain sensor data from a set of sensor associated with one or machines, apply data exploration techniques on the sensor data to automatically process sensor data to identify a subset of feature sensors from the available set of feature sensors, apply an unsupervised machine-learning technique to the identified subset of feature sensors and the target sensor to learn a set of pair-wise univariate models, and determine whether and how an anomaly occurs in the operation of the one or more machines based on the set of pair-wise univariate models.

Method and system for unsupervised anomaly detection and accountability with majority voting for high-dimensional sensor data

One embodiment provides a system for facilitating fault diagnosis. During operation, the system collects current signals associated with a physical object which comprises a rotating machine. The system demodulates the collected signals to obtain current envelope signals, which eliminates fundamental frequencies and retains fault-related frequencies. The system resamples the current envelope signals, which converts the fault-related frequencies to constant frequency components. The system enlarges, by a fault-amplifying convolution layer, the resampled envelope signals to obtain fault information. The system provides the fault information as input to a deep convolutional neural network (CNN). The system generates, by the deep CNN, an output which comprises the fault diagnosis for the physical object.

Deep hybrid convolutional neural network for fault diagnosis of wind turbine gearboxes

One embodiment can provide a system for detecting faults in a machine. During operation, the system can obtain a dynamic signal associated with the machine, apply one or more signal-processing techniques to the dynamic signal to obtain frequency, amplitude, and/or time-frequency information associated with the dynamic signal, extract motion-insensitive features from the obtained frequency, amplitude, and/or time-frequency information associated with the dynamic signal, and determine whether a fault occurs in the machine based on the extracted features.

Growing community of inventors

Mountain View, CA, United States of America

Fangzhou Cheng