Accenture Global Solutions Limited

Takuya Kudo

Makoto Murai

Shin Moriga

Motoaki Hayashi

Atsushi Suyama

Tim Wu

Sean Michael O'Connor

Takahiro Sasaki

Takafumi Mizuno

Sayaka Tanaka

Yukihisa Fujita

Miku Yoshio

Kana Cornett

Akira Yotsuyanagi

Congwei Dang

Takaaki Haraguchi

Makoto Yomosa

The present disclosure describes a method of controlling a manufacturing system using multivariate time series and includes storing recording data as a plurality of time series, each time series having a first recorded value and a final recorded value, interpolating, within a first time window, missing values in the plurality of time series using a Bayesian model, the missing values falling between a first and an end time of the respective time series, storing the interpolated values as prediction data, each interpolated value including an uncertainty, loading recorded data of a second time window, loading prediction data of the second time window, predicting, using the Bayesian model, values for each time series that is absent recorded data and prediction data, storing the predicted values, each prediction value including an uncertainty, and adjusting a device that generates the recorded data based on the prediction values within the second time window.

Prediction method and system for multivariate time series data in manufacturing systems

The present disclosure describes a computer-implemented method for detecting anomalies during lot production, wherein the products within a production lot are processed according to a sequence of steps that include manufacturing steps and one or more quality control steps interspersed among the manufacturing steps, the method comprising: obtaining process quality inspection data from each of the one or more quality control steps for a first production lot; obtaining product characteristics data for the products in the first production lot after the final step in the sequence; training a Gaussian process regression model using the process quality inspection data and the product characteristics data from the first production lot; generating a predictive distribution of the product characteristics data using the Gaussian process regression model that uses a bathtub kernel function; obtaining process quality inspection data from each of the quality control steps for a second production lot; identifying anomalies in the second production lot using the predictive distribution of the product characteristics data and the process quality inspection data from the second production lot; if no anomalies are detected in the second production lot, updating the Gaussian process regression model using the process quality inspection data from the second production lot; setting target values for one or more values in the process quality inspection data based on the predictive distribution of the product characteristic; and adjusting settings of one or more manufacturing steps based on the target values.

Anomaly detection method and system for manufacturing processes

The present disclosure describes a method of controlling a manufacturing system using multivariate time series, the method comprising: recording data from one or more devices in the manufacturing system; storing the recorded data in a data storage as a plurality of time series, wherein each time series has a first recorded value corresponding to a first time and a final recorded value corresponding to an end of the time series; interpolating, within a first time window, missing values in the plurality of time series using a Bayesian model, wherein the missing values fall between the first and end time of the respective time series; storing the interpolated values as prediction data in a prediction storage, wherein the interpolated values include the uncertainty of each interpolated value; loading the recorded data that fall within a second time window from the data storage; loading prediction data from the prediction storage that fall within the second time window and for which no recorded data are available; optimizing the parameters of the Bayesian model using the loaded recorded data and the prediction data; predicting, using the Bayesian model, values for each of the time series for which loaded recorded and prediction data are not available; storing the predicted values as prediction data in the prediction storage, wherein the prediction values include the uncertainty of each prediction value; and adjusting one or more of the devices that generate the recorded data based on the prediction data within the second time window.

Implementations include receiving two or more time-series data sequences representative of a target process executed within a physical environment, executing automated time-series process segmentation to provide a plurality of subsequence segments for each of the two or more time-series data sequences, each subsequence segment corresponding to a phase of the target process, processing the two or more subsequence segments using at least one time-series transformation to provide a feature data set for each subsequence segment, applying each feature data set to provide time-series models for anomaly detection and forecasting, respectively, each time-series model being provided as one of a recurrent neural network (RNN), a convolution neural network (CNN), and a generative adversarial network (GAN), determining anomaly scores based on the time-series models, and selectively providing an alert to one or more users, each alert indicating at least one anomaly and a respective probability.

Intelligent time-series analytic engine

Implementations include receiving image data representative of images of items within a physical environment and depicting defects in at least one item, providing one or more of a set of augmented images using image augmentation based on the image data and a set of synthetic images using ML-based image synthesis based on the image data, processing one of the set of augmented images and the set of synthetic images using an ML model to provide a set of defect characteristics representative of defects in the at least one item, providing one or more root causes of each of the one or more defects by processing the set of defect characteristics and ancillary data, the ancillary data representative of the physical environment, and generating one or more alerts based on the one or more root causes for remediation of at least one root cause of the one or more defects.

Intelligent defect detection from image data

A rating prediction engine builds and applies models to predict ratings based on an analysis of textual reviews and comments. The engine can build multiple models simultaneously through distributed parallel model building that employs deep convolutional neural networks (CNNs). The engine can also incorporate user moment feature data, including user status and context information, to provide better performance and more accurate predictions. The engine can also employ heuristic unsupervised pre-training and/or adaptive over-fitting reduction for model building. In some instances, the techniques described herein can be used in a service to predict personalized ratings for reviews or other published items, in instances where the original author of the item did not include a rating and/or in instances where the publication channel does not provide a mechanism to enter ratings.

Network rating prediction engine

A system for automatic ordering of products comprises a receiver to receive stock parameters including a number of products sold till a point in time, an expiration duration for usage of products, or a number of products available in an inventory at the time. The system also includes a stock analyzer to determine a stock-out probability factor based on the number of products sold and estimated short term sales, determine a disposal probability factor based on the number of products sold, the expiration duration and estimated long term sales, ascertain a risk balance factor indicative of a ratio of the disposal probability factor to the stock-out probability factor, determine a number of products to replenish the inventory, based on the stock-out probability factor, the disposal probability factor, and the risk balance factor, and place a purchase order to a vendor for acquiring the number of products for replenishing the inventory.

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Kirkland, WA, United States of America

Takuya Kudo