Large-scale multi-detector predictive modeling

Patent metadata

Field	Value
Publication number	US-9561810-B2
Application number	US-201313873859-A
Country	US
Kind code	B2
Filing date	Apr 30, 2013
Priority date	Jan 11, 2013
Publication date	Feb 7, 2017
Grant date	Feb 7, 2017

Abstract

Predicting operational changes in a multi-detector environment includes generating, via a computer processing device, a factor matrix for each univariate time series data in a set of sparse time series data collected from data sources, identifying a subset of the time series data as a feature selection based on application of a loss function, and generating a predictive model from the subset of the time series data.

First claim

What is claimed is:

1. A system for predictive modeling, the system comprising:
a computer processing device communicatively coupled to data sources operating in a railroad environment; and
logic executable by the computer processing device, the logic configured to implement a method, the method including:
generating a factor matrix for each univariate time series data in a set of sparse time series data collected from the data sources, the time series data including at least one of temperature, optical geometry, load, and acoustic data;
identifying a subset of the time series data as a feature selection based on application of a loss function;
generating a predictive model from the subset of the time series data; and
generating, from the predictive model, a prediction indicating an amount of time before a failure occurs with respect to a component of the railroad environment.

2. The system of claim 1 , wherein the logic is further configured to implement:
receiving new data from the data sources;
determining a change in a failure rate based on a one-sample weighted rank test; and
upon determining the change exceeds a defined threshold value, updating the predictive model based on the change.

3. The system of claim 2 , wherein the predictive model is updated using a Bayesian inference.

4. The system of claim 2 , wherein the defined threshold value is selected by a user of the computer processing device.

5. The system of claim 1 , wherein the data sources are detectors including at least one of:
a machine vision detector;
a wheel impact load detector;
an optical geometry detector;
a truck performance detector;
an acoustic bay detector;
a hot box detector;
a warm bearing detector;
a hot wheel detector; and
a cold wheel detector.

6. The system of claim 1 , wherein the data sources are configured to capture time, physical location, and object location regarding corresponding subjects of measurement.

7. The system of claim 1 , wherein the factor matrix is generated using a supervised matrix factorization technique.

Assignees

• IBM

Inventors

• Hampapur Arun
• He Qing
• Li Hongfei
• Li Zhiguo
• Parikh Dhaivat P

Classifications

• G06F30/20

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• B61L23/042

  Track changes detection · CPC title

• G06Q10/0635

  Risk analysis of enterprise or organisation activities · CPC title

• B61L23/045

  Rail wear · CPC title

• B61L23/047

  Track or rail movements · CPC title