ASSET HEALTH MONITORING

• US 20130096699A1
• Filed: 06/07/2011
• Published: 04/18/2013
• Est. Priority Date: 06/21/2010
• Status: Abandoned Application

• Current Assignee: Rolls-Royce Controls and Data Services Limited
• Original Assignee: Optimized Systems and Solutions Incorporated (Rolls-Royce Holdings Plc)
• Inventors: Sundaram, Srini, McDonald, Ken
• Application Number:
  US13/704,110
• Publication Number:
  US 20130096699A1
• Time in Patent Office: Days
• US Class Current: 700/79
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/37545 References to be compared vary with evolution of measured signals, auto-calibrate
• CPC Class Codes: G05B 2219/50197 Signature analysis, store working conditions, compare with actual
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures

Abstract

A machine management system, including one or more sensors arranged to take readings of one or more operating variables for a machine. Data processing equipment is arranged to receive data indicative of said operating variable readings for the machine and to analyse the received operational data to determine a probability distribution there-for. The processing equipment includes one or more modules of machine-readable code for assigning a value to one or more Stable distribution parameters in dependence on the received operational data, and a departure in the machine operation from a predetermined normal operating condition is determined from the assigned distribution parameter.

Claims

1. A machine management system, comprising:

one or more sensors arranged to take readings of one or more operating variables for a machine;

data processing equipment arranged to receive data indicative of said operating variable readings for the machine and analyse the received operational data to determine a probability distribution there-for,

wherein the processing equipment comprises one or more modules of machine-readable code for assigning a value to one or more Stable distribution parameters in dependence on the received operational data, and a departure in the machine operation from a predetermined normal operating condition is determined from the assigned distribution parameter.

2. A machine management system according to claim 1, wherein the or each Stable distribution parameter is one of an index of stability, a skewness parameter, a scale parameter and a location parameter.

3. A machine management system according to claim 1, wherein a normal operating condition is defined using a Gaussian distribution and the departure in machine operation from the normal operating condition is based upon a difference between the assigned value of the Stable distribution parameter and a distribution parameter value according to said Gaussian distribution.

4. A machine management system according to claim 1, wherein the Stable distribution parameter defines a distribution having greater kurtosis than that of a Gaussian distribution.

5. A machine management system according to claim 1, wherein the Stable distribution parameter accommodates asymmetric behaviour in the data.

6. A machine management system according to claim 1, wherein a threshold parameter value is predetermined and an abnormality in machine operation is determined when said assigned distribution parameter meets or exceeds said threshold value.

7. A machine management system according to claim 1, wherein four Stable distribution parameters are assigned in dependence on the received operational data.

8. A machine management system according to claim 1, wherein the Stable distribution parameter is an index of stability, α

and a departure in the machine operation from a predetermined normal operating condition is determined for the condition α

<

2.

9. A machine management system according to claim 1, wherein an initial value of the, or each, Stable distribution parameter is estimated using a quantile-based method.

10. A machine management system according to claim 9, wherein said initial value estimation is subsequently refined using a regression-based method.

11. A machine management system according to claim 1, comprising alerting means, wherein upon determination of a departure in the machine operation from a predetermined normal operating condition, the processing equipment outputs an alert signal via said alerting means.

12. A machine management system according to claim 1, comprising a scheduler for scheduling inspection, maintenance, or overhaul work in dependence on output of a signal from said processing equipment indicative of the determination of a departure from said normal operating condition.

13. A method of determining an abnormality in the operation of a machine, comprising:

receiving data indicative of operating variable readings for the machine;

analysing the received operational data to determine a probability distribution there-for,

approximating the probability distribution by way of a Stable distribution by assigning a value to a plurality of Stable distribution parameters in dependence on the received operational data, and

determining a departure in the machine operation from a predetermined normal operating condition based upon the assigned distribution parameter value.

14. A method according to claim 13, wherein the determination of an abnormal operating condition comprises comparing the assigned distribution parameter value to a predetermined normal parameter value.

15. A data carrier comprising machine-readable instructions for the control of one or more processors to perform the method of claim 13.