METHOD OF DETECTING ANOMALIES ON APPLIANCES AND SYSTEM THEREOF

• US 20170315855A1
• Filed: 05/02/2016
• Published: 11/02/2017
• Est. Priority Date: 05/02/2016
• Status: Abandoned Application

• Current Assignee: AGT International GMBH, Technische Universitat Munchen
• Original Assignee: AGT International GMBH, Technische Universitat Munchen
• Inventors: DOBLANDER, Christoph, JACOBSEN, Hans-Arno
• Application Number:
  US15/144,101
• Publication Number:
  US 20170315855A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 11/0709 in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
• CPC Class Codes: G06F 11/0736 in functional embedded systems, i.e. in a data processing system designed as a combination of hardware and software dedicated to performing a certain function testing or monitoring of automated control systems G05B23/02
• CPC Class Codes: G06F 11/0754 by exceeding limits
• CPC Class Codes: G06F 11/0757 by exceeding a time limit, i.e. time-out, e.g. watchdogs
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A method, system and computer program product, the method comprising: obtaining transition probabilities, each transition probability associated with transition of a home appliance between states; receiving sensor readings indicating behavior of the home appliance; identifying by the processor a transition event occurring in the sensor readings; determining by the processor a source cluster and a destination cluster associated with the transition event; determining by the processor a duration indicator associated with the transition event; determining by the processor a transition probability by looking up in the transition probabilities, a probability associated with the duration indicator, the source cluster and the destination cluster; comparing by the processor the transition probability to a threshold; and responsive to the transition probability exceeding a threshold, providing an indication of abnormal behavior of the home appliance to a user.

Claims

1. A computer-implemented method for identifying anomalies in data streams using a processor operatively connected to a memory, the method comprising:

receiving sensor readings associated with a home appliance of a home appliance type;

clustering by a processor the sensor readings into a plurality of clusters;

extracting by the processor from the sensor readings transition features associated with a transition, in accordance with the plurality of clusters, the transitions indicating state changes in the home appliance, each state associated with a cluster; and

based on the transition features, determining transition probabilities between states of the home appliance for a plurality of transition time indicators and accommodating the transition probabilities in the memory,

wherein the transition probabilities are adapted for detecting anomalies in transitions occurring in further sensor readings, thus identifying abnormal behavior of another appliance of the home appliance type.

2. The method of claim 1, wherein clustering is performed by a K-means clustering process.

3. The method of claim 1, wherein clustering is performed by a DBscan clustering process.

4. The method of claim 1, wherein determining the transition probabilities comprises:

indicating a time duration for each transition;

determining number of transitions for each combination of source and destination for each time duration; and

normalizing the number of transitions.

5. The method of claim 3, wherein determining the number of transitions for each time duration comprises Markov chain sampling.

7. The method of claim 5, wherein the duration indicator is a discretized transition duration associated with the transition event.

9. The method of claim 5, wherein the sensor readings refer to at least one item selected from the group consisting of:

power consumption;

current;

voltage;

fluid flow;

temperature; and

humidity.

10. The method of claim 5, wherein obtaining the transition probabilities comprises:

receiving sensor readings associated with a home appliance;

clustering the sensor readings into a plurality of clusters;

extracting from the sensor readings transition features associated with a transition, in accordance with the plurality of clusters, the transitions indicating state changes in the home appliance, each state associated with a cluster; and

based on the transition features, determining transition probabilities between states of the home appliance for a plurality of transition time indicators.

11. The method of claim 10, wherein clustering is performed by a K-means clustering process.

12. The method of claim 10, wherein clustering is performed by a process selected from the group consisting of:

DBscan, K-Histograms and Ward'"'"'s Method.

13. The method of claim 10, wherein determining the transition probabilities comprises:

indicating a time duration for each transition;

determining number of transitions for each combination of source and destination for each time duration; and

normalizing the number of transitions.

14. The method of claim 13, wherein determining the number of transitions for each time duration comprises Markov chain sampling.

6. A computer-implemented method for identifying anomalies in data streams indicating behavior of a home appliance using a processor operatively connected to a memory, the method comprising:

obtaining transition probabilities, each transition probability associated with transition of a home appliance between states;

receiving sensor readings indicating behavior of the home appliance;

identifying by the processor a transition event occurring in the sensor readings;

determining by the processor a source cluster and a destination cluster associated with the transition event;

determining by the processor a duration indicator associated with the transition event;

determining by the processor a transition probability by looking up in the transition probabilities, a probability associated with the duration indicator, the source cluster and the destination cluster;

comparing by the processor the transition probability to a threshold; and

responsive to the transition probability exceeding a threshold, providing an indication of abnormal behavior of the home appliance to a user.

8. The method of claim 6, wherein the discretized transition duration is an index of a Fibonacci number larger than the transition duration.

15. A computerized system for projecting a machine learning model, the system comprising a processor, wherein:

the processor is configured to obtain transition probabilities, each transition probability associated with transition of a home appliance between states;

the processor is configured to receive sensor readings indicating behavior of the home appliance;

the processor is configured to identify by the processor a transition event occurring in the sensor readings;

the processor is configured to determine a source cluster and a destination cluster associated with the transition event;

the processor is configured to determine a duration indicator associated with the transition event;

the processor is configured to determine a transition probability by looking up in the transition probabilities, a probability associated with the duration indicator, the source cluster and the destination cluster;

the processor is configured to compare the transition probability to a threshold; and

the processor is configured to provide an indication of abnormal behavior of the home appliance to a user determine, responsive to the transition probability exceeding a threshold.

16. The system of claim 15, wherein the duration indicator is a discretized transition duration associated with the transition event and wherein the discretized transition duration is an index of a Fibonacci number larger than the transition duration.

17. The system of claim 15, wherein obtaining the transition probabilities comprises:

receiving sensor readings associated with a home appliance;

clustering the sensor readings into a plurality of clusters;

extracting from the sensor readings transition features associated with a transition, in accordance with the plurality of clusters, the transitions indicating state changes in the home appliance, each state associated with a cluster; and

based on the transition features, determining transition probabilities between states of the home appliance for a plurality of transition time indicators.

18. The system of claim 17, wherein clustering is performed by a process selected from the group consisting of:

DBscan, K-Histograms and Ward'"'"'s Method.

19. The system of claim 17, wherein determining the transition probabilities comprises:

indicating a time duration for each transition;

determining number of transitions for each combination of source and destination for each time duration; and

normalizing the number of transitions.

20. A computer program product comprising a computer readable storage medium retaining program instructions, which program instructions when read by a processor, cause the processor to perform a method comprising:

obtaining transition probabilities, each transition probability associated with transition of a home appliance between states;

receiving sensor readings indicating behavior of the home appliance;

identifying by the processor a transition event occurring in the sensor readings;

determining by the processor a source cluster and a destination cluster associated with the transition event;

determining by the processor a duration indicator associated with the transition event;

determining by the processor a transition probability by looking up in the transition probabilities, a probability associated with the duration indicator, the source cluster and the destination cluster;

comparing by the processor the transition probability to a threshold; and

responsive to the transition probability exceeding a threshold, providing an indication of abnormal behavior of the home appliance to a user.