FACILITY STATUS MONITORING METHOD AND FACILITY STATUS MONITORING DEVICE

• US 20150213706A1
• Filed: 07/05/2013
• Published: 07/30/2015
• Est. Priority Date: 08/29/2012
• Status: Abandoned Application

• Current Assignee: Hitachi, Ltd.
• Original Assignee: Hitachi, Ltd.
• Inventors: Shibuya, Hisae, Maeda, Shunji, Bai, Jie
• Application Number:
  US14/416,466
• Publication Number:
  US 20150213706A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• CPC Class Codes: G08B 21/182 Level alarms, e.g. alarms responsive to variables exceeding a threshold
• CPC Class Codes: G08B 21/185 Electrical failure alarms

Abstract

In a facility such as a plant, error detection can be performed by using characteristic amounts based on a statistical probability characteristic, but when sensor data is acquired at long sampling intervals for reducing costs, those intense changes cannot always be caught. Furthermore, when the sensor sampling time is not synchronized with the start of a sequence, a time difference occurs between sensor data obtained in the same sequence at different times, so it is not possible to determine a statistical probability characteristic for areas of intense change. Therefore, with the present invention a statistical probability characteristic for a time period to be monitored is calculated by estimating the sensor data that cannot be obtained, and error detection is performed on the basis of that statistical probability characteristic with respect to sequences with intense changes. Thus, it is possible to perform error detection with respect to sequences with intense changes.

Claims

1. A facility status monitoring method for sensing an abnormality of a plant or facility, comprising the steps of:

inputting a sensor signal that is intermittently outputted from a sensor attached to a plant or facility, and event signals associated with initiation and termination respectively of an activation sequence or suspension sequence of the plant or facility during the same period as the period during which the sensor signal is outputted;

cutting a sensor signal, which is associated with a section between the event signal of the initiation of the activation sequence or suspension sequence and the event signal of the termination of the activation sequence or suspension sequence, from the inputted sensor signal;

estimating signal values at certain times of the cut sensor signal, and probability distributions of the respective signal values;

extracting a feature quantity on the basis of the estimated probability distributions; and

sensing an abnormality of the plant or facility on the basis of the extracted feature quantity.

2. The facility status monitoring method according to claim 1,

wherein estimating signal values at certain times of the cut sensor signal and probability distributions of the respective signal values is performed by:

synchronizing the cut sensor signal with times that are obtained with the event signal of the initiation of the activation sequence or suspension sequence as an origin;

determining times at which data items of the synchronized sensor signal are estimated;

estimating sensor data items to be observed at the determined times; and

estimating probability distributions of the estimated sensor data items.

3. The facility status monitoring method according to claim 2,

wherein the technique for estimating sensor data items is selected from among a plurality of techniques displayed on a screen, and the sensor data items are estimated based on the selected technique.

4. The facility status monitoring method according to claim 2,

wherein information on the estimated sensor data items is displayed on the screen.

5. The facility status monitoring method according to claim 1,

wherein sensing an abnormality of the plant or facility on the basis of the extracted feature quantity is achieved by using a sensor signal, which is obtained when the plant or facility operates normally, to determine a normal space or decision boundary for the sensor signal, deciding whether the extracted feature quantity falls within or inside the determined normal space or decision boundary, and sensing the abnormality of the plant or facility.

6. A facility status monitoring device for sensing an abnormality of a plant or facility, comprising:

a data preprocessing unit that inputs a sensor signal, which is intermittently outputted from a sensor attached to a plant or facility, and event signals associated with initiation and termination respectively of an activation sequence or suspension sequence of the plant or facility, cutting a sensor signal, which is associated with a section between the event signal of the initiation of the activation sequence or suspension sequence and the event signal of the termination of the activation sequence or suspension sequence, from the inputted sensor signal, and synchronizing the cut sensor signal with times that are obtained with the event signal of the initiation of the activation sequence or termination sequence as an origin;

a probability distribution estimation unit that estimates signal values at certain times of the sensor signal, which is processed by the data preprocessing unit, and probability distributions of the respective signal values;

a feature quantity extraction unit that extracts a feature quantity on the basis of the probability distributions estimated by the probability distribution estimation unit;

an abnormality detector that detects an abnormality of the plant or facility on the basis of the feature quantity extracted by the feature quantity extraction unit; and

an input/output unit that includes a screen on which information to be inputted or outputted is displayed, and displays on the screen information concerning the abnormality of the plant or facility detected by the abnormality detector.

7. The facility status monitoring device according to claim 6,

wherein the probability distribution estimation unit includes:

an estimation time determination block that determines times at which data items of the cut sensor signal, which is synchronized with the times that are obtained with the event signal of the initiation of the activation sequence or suspension sequence as an origin, are estimated;

a sensor data estimation block that estimates sensor data items to be observed at the times determined by the estimation time determination block; and

a statistical probability distribution estimation block that estimates statistical probability distributions of the sensor data items estimated by the sensor data estimation block.

8. The facility status monitoring device according to claim 7,

wherein the input/output unit displays on the screen a plurality of techniques according to which the sensor data estimation block estimates sensor data items, and the sensor data estimation block estimates the sensor data items according to the technique selected on the screen from among the plurality of displayed techniques.

9. The facility status monitoring device according to claim 7,

wherein the input/output unit displays on the screen information concerning the sensor data items estimated by the sensor data estimation block.

10. The facility status monitoring device according to claim 6,

wherein the abnormality detector includes a learning unit that uses a sensor signal, which is obtained when the plant or facility operates normally, to determine a normal space or decision boundary for the sensor signal, and an abnormality sensing unit that decides whether the feature quantity extracted by the feature quantity extraction unit falls within or inside the determined normal space or decision boundary, and senses an abnormality of the plant or facility.