International Business Machines Corporation

Seefar Technologies, Incorporated

Donnelly Corporation

Noodle Analytics, Inc.

Matthew Denesuk

Ying Tat Leung

Axel Hochstein

Hyung-il Ahn

Reiner Kraft

Eugenie V Uhlmann

Gal Ashour

Brian K Blanchard

Andrew Stephen Tomkins

Daniel Gruhl

Arnon Amir

Sridhar Rajagopalan

Kevin Snow McCurley

Anoop Agrawal

John P Cronin

Kevin C McCarthy

Donald R Uhlmann

Juan Carlos Lopez Tonazzi

Gimtong Teowee

Robert Steven LeCompte

Steve R Kennedy

Kavita Chavda

Jason Yeong Zien

Joerg Meyer

Leslie Mark Ernest

Erin Hingst

Felicia A Hochheiser

Joanne L Martin

Amon Amir

Moonish Badaloo

Nayan Ketak Dharamshi

Rohan Jha

Harshini Mogili

Maryclay Smith

A system is presented for predicting the amount of energy required by a manufacturing plant over a period of time, and then providing a recommendation on the best amount of energy to sell at each period, taking into consideration the energy requirements based on the production schedule, the price of energy, possible penalties for over selling energy, and other factors. A user interface is provided to present relevant information to a user, allow the user to plan the use of energy, and determine the energy offering for sale in each period. Based on a known future production schedule of a steel mill, implementations provide a forecast of electrical energy usage per hour over the same time horizon as the production schedule. Based on the forecast and electricity selling and billing rules, a recommended amount of energy to be sold to the market by the hour is presented with the corresponding offer price.

Reducing the cost of electrical energy in a manufacturing plant

Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component are provided herein. A method includes splitting an input time series pertaining to a vehicular component across a fleet of multiple vehicles into multiple sub-time series, wherein each sub-time series comprises multiple data points of the input time series that correspond to measurements derived from the vehicular component; determining a weight applied to each of the sub-time series based on a pre-determined weight associated with the input time series; applying a failure or non-failure classification label to each of the sub-time series and the input time series; calculating a performance measure for the input time series; determining an updated weight associated with the input time series; and generating an estimate of failure risk for the vehicular component based on the classification label applied to each input time series and the updated weight.

Generating estimates of failure risk for a vehicular component

Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component in situations of high-dimensional and low sample size data are provided herein. A method includes splitting a first input time series comprising multiple data points derived from a vehicular component across a fleet of multiple vehicles into multiple sub-time series; generating a first failure status predicting function of a first selected sub-time series; deleting, from the first input time series, the portion of the data points that corresponds to the first selected sub-time series; repeating the preceding two steps for a second selected sub-time series; generating a second failure status predicting function of each selected sub-time series; applying each second failure status predicting function to a second input time series to calculate prediction of failure values; and identifying the largest prediction of failure value as an estimate of failure risk for the vehicular component.

Generating estimates of failure risk for a vehicular component in situations of high-dimensional and low sample size data

Methods, systems, and computer program products for generating wear-based indicators are provided herein. A method includes assigning a failure label to each data point associated with a component associated with a failure-caused component replacement within a pre-specified number of runtime hours of the failure-caused component replacement; assigning a non-failure label to each data point associated with a failure-caused component replacement and not within the pre-specified number of runtime hours; assigning a non-failure label to each data point associated with a scheduled component replacement; assigning a non-failure label to each data point associated with an actively running instance of the component as yet to be replaced; estimating a failure probability for the component over a pre-specified future runtime; determining a cumulative hazard function for the component based on the failure probability; and generating a cumulative wear-based indicator for the component by executing a regression function based on the cumulative hazard function.

Generating cumulative wear-based indicators for vehicular components

Methods, systems, and computer program products for generating a vehicular component replacement policy are provided herein. A method includes, for each of multiple lifetime wear indicator functions associated with a vehicular component, wherein each lifetime wear indicator function comprises a transformed time scale plotting wear indicator values over a period of time, determining multiple corresponding candidate threshold values on the transformed time scale; calculating: a survival probability function for the vehicular component based on each transformed time scale, an average runtime of the vehicular component prior to failure, and an average runtime of the vehicular component prior to a scheduled replacement; calculating an economic criterion value for each given threshold value based on the above calculations and one or more economic parameters; generating the replacement policy to include (i) the lifetime wear indicator function that optimizes the economic criterion value and (ii) the corresponding threshold value.

Integrating economic considerations to develop a component replacement policy based on a cumulative wear-based indicator for a vehicular component

Methods, systems, devices and computer program products for planning for failures of vehicular components are provided herein. A method includes obtaining a first set of data of maintenance events recorded for multiple vehicular components across multiple vehicles in a fleet; obtaining a second set of data of maintenance work orders performed on the vehicular components; obtaining a third set of data of measurements taken in connection with the vehicular components; analyzing (i) the first set of data and (ii) the second set of data to identify component failure events associated with the multiple vehicular components; determining failure indicators for each of the vehicular components in each of the vehicles in the fleet based on the second set of data, the third set of data, and the identified component failure events; and outputting the indicators in multiple visualized forms, each representing one of multiple levels of granularity.

Monitoring and planning for failures of vehicular components

Methods, systems, and computer program products for generating wear-based indicators for vehicular components are provided herein. A method includes assigning a failure class label to each data point, from multiple data points derived from measurements associated with a vehicular component across a fleet of vehicles, that is within a pre-specified number of runtime hours of a replacement; assigning a non-failure class label to each data point not within the pre-specified number of runtime hours of a replacement and each data point associated with a component yet to be replaced; estimating a failure probability at each data point over a pre-specified future runtime of the component based on the assigned class label; determining a cumulative hazard function for the vehicular component based on the failure probability; and generating a cumulative wear-based indicator for the vehicular component by executing a regression function at a given time based on the cumulative hazard function.

A solution for managing a service engagement is provided. A service delivery model for the service engagement is defined within an engagement framework. The engagement framework, and consequently the service delivery model, can include a hierarchy that comprises a service definition, a set of service elements for the service definition, and a set of element tasks for each service element. The set of element tasks can be selected from a set of base tasks, each of which defines a particular task along with its input(s), output(s), and related asset(s). As a result, service engagements can be managed in a consistent manner using a data structure that promotes reuse and is readily extensible.

Service engagement management using a standard framework

In a data mining system, data is gathered into a data store using, e.g., a Web crawler. The data is classified into entities. Data miners use rules to process the entities and append respective keys to the entities representing characteristics of the entities as derived from rules embodied in the miners. With these keys, characteristics of entities as defined by disparate expert authors of the data miners are identified for use in responding to complex data requests from customers.

Knowledge-based data mining system

A computing system and method receive a query; separate a plurality of information sources into individual elements of content (EOC); tag each EOC with metadata that indicate source, date, and other relevant information; pattern match each EOC; calculate the respective distance function from every EOC to every other EOC; and output EOC to a set of virtual buffers () containing appropriately related EOC less than a given distance value. The method further creates virtual summary buffers (); then concatenates the EOC in each virtual buffer (); applies a comparative analysis filter () to remove redundant sub-elements; and presents the results as summary digests ().

Synthesizing information-bearing content from multiple channels

A method receives a query; separates a plurality of information sources into individual elements of content (EOC); tags each EOC with metadata that indicate source, date, and other relevant information; pattern matches each EOC; calculates the respective distance function from every EOC to every other EOC; and outputs EOC to a set of virtual buffers () containing appropriately related EOC less than a given distance value. The method further creates virtual summary buffers (); concatenates the EOC in each virtual buffer (); applies a comparative analysis filter () to remove redundant sub-elements extracts context-preserving EOC, determined based upon a respective degree of corroboration of the context preserving EOC, from each respective concatenated EOC. The method synthesizes the content-preserving EOC extracted from each respective concatenated EOC into summary digests; and presents the results as summary digests ().

An amusement article for a domestic animal comprising an outer textile casing defining a shape in the form of a small article which can be carried by a domestic animal, an inner filling, and a microbe-inhibiting agent or property applied to at least one of the outer textile casing and the inner filling. The toys may be fabricated in various shapes, designs and styles, e.g., animals, bones, hearts, geometric shapes, etc. A process for applying the microbe-inhibiting agent or property to at least one of the outer textile casing and the inner filling is provided. Application methods include spraying, dipping, brushing, and rolling the microbe-inhibiting agent or property onto at least one of the outer textile casing and the inner filling.

Amusement articles possessing microbe-inhibiting properties

A computing system and method receive a query; separate a plurality of information sources into individual elements of content (EOC); tag each EOC with metadata that indicate source, date, and other relevant information; pattern match each EOC; calculate the distance function from every EOC to every other EOC; and output EOC to a set of virtual buffers () containing appropriately related EOC less than a given distance value. The method further creates virtual summary buffers (); then concatenates the EOC in each virtual buffer (); applies a comparative analysis filter () to remove redundant sub-elements; and presents the results as summary digests ().

In a data mining system, data is gathered into a data store using, e.g., a Web crawler. The data is classified into entities. Data miners use rules to process the entities and append respective keys to the entities representing characteristics of the entities as derived from expert rules embodied in the miners. With these keys, characteristics of entities as defined by disparate expert authors of the data miners are identified for use in responding to complex data requests from customers.

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San Jose, CA, United States of America

Matthew Denesuk