Abnormality diagnosis device for rotary apparatus

Abstract

This abnormality diagnosis device for a rotary apparatus includes: a vibration sensor that measures vibration of a rotary apparatus; an operation database that collects and accumulates measurement data of the vibration sensor; a rotary apparatus information database that records at least a model, specifications, and performance information of the rotary apparatus; a maintenance work history database that records a maintenance work history of the rotary apparatus; and a diagnosis processing unit that diagnoses an abnormality of the rotary apparatus by using data of the respective databases. The diagnosis processing unit holds a diagnosis model for each model and performs diagnosis.

Description

Abnormal Diagnosis Device for Rotating Equipment

[0001] The present invention relates to an abnormal diagnosis device for rotating equipment such as pumps, blowers, and agitators, which diagnoses abnormalities in rotating equipment.

[0002] As a method for diagnosing failures of multiple rotating devices such as fans and pumps, Patent Document 1 describes a method in which, in advance, one or both of the synthetic noise and synthetic vibration during normal operation of multiple devices are measured and stored, and the stored normal values are compared with the measured values during the normal operation of the multiple devices, and the abnormal state of the devices is detected from the comparison results.

[0003] Further, in the column of the conventional technology of Patent Document 1, when diagnosing failures of multiple devices of a single or multiple types, it is described that abnormalities of the devices are detected using the noise values or vibration values measured for each device.

[0004] Japanese Patent Application Laid-Open No. 5-203542

[0005] In Patent Document 1, the noise and vibration of rotating equipment are compared with normal values to diagnose abnormalities, resulting in inferior diagnostic accuracy.

[0006] An aspect of the present invention is to provide an abnormal diagnosis device for rotating equipment with excellent diagnostic accuracy for abnormalities in rotating equipment.

[0007] The present invention has the following gist.

[0008] [1] A vibration sensor that measures the vibration of rotating equipment, an operation database that collects and stores the measurement data of the vibration sensor, a rotating equipment information database that records at least the type, specifications, and performance information of the rotating equipment, a maintenance work history database that records the maintenance work history of the rotating equipment, and a diagnostic processing unit that diagnoses abnormalities in the rotating equipment using the data of each of the above databases. An abnormal diagnosis device for rotating equipment, wherein the diagnostic processing unit holds a diagnostic model for each type and diagnoses.

[0009] [2] The abnormal diagnosis device for rotating equipment according to [1], wherein each of the above databases can identify individual rotating equipment with a common identification symbol.

[0010] [3] The abnormality diagnosis device for rotating machinery according to [1] or [2], wherein the diagnostic processing unit has the function of extracting fault response maintenance work for rotating machinery from the maintenance work history database, identifying the time and type of fault, and training the diagnostic model by machine learning the change patterns of diagnostic data before the fault occurred.

[0011] According to one aspect of the present invention, abnormalities can be detected with high accuracy by applying a diagnostic model that is appropriate to the type and specifications of the rotating equipment.

[0012] In one aspect of the present invention, by using machine learning to train a diagnostic model with vibration data based on past failure cases, problems can be detected more accurately and earlier than with threshold judgment.

[0013] In one aspect of the present invention, it becomes possible to present past cases with similar patterns, enabling more accurate fault response and streamlining recovery operations.

[0014] This is a schematic diagram of the abnormality diagnosis device for rotating machinery according to an embodiment of the present invention. This is a flowchart illustrating the abnormality diagnosis method.

[0015] Embodiments of the present invention will now be described in detail with reference to the drawings. The abnormality diagnosis device for rotating machinery according to this embodiment is for diagnosing abnormalities based on vibrations of rotating machinery.

[0016] As shown in Figure 1, in a water treatment system equipped with multiple rotating devices 1, vibration sensors 2 are installed on each rotating device 1. If the rotating device is a pump, depending on the type of pump, it may be better to measure multiple locations such as the motor and pump sections, so one or more vibration sensors 2 are installed on each rotating device 1. (Pumps that do not cause harm to water treatment even if they fail and stop, such as pumps with backup units and automatic switching functions in case of failure, may be excluded.) The vibration sensor 2 may be equipped with an acceleration sensor and output acceleration data directly, or it may process acceleration data over a certain period of time (0.1 to 5 seconds) and output values ​​such as RMS value, peak acceleration, and peak frequency value obtained by frequency analysis.

[0017] Since data that has undergone statistical processing and analysis is easier to extract features related to failures from, sensors with data processing capabilities are preferable because they reduce the load on the diagnostic device 6, which will be described later.

[0018] The data from the vibration sensor 2 is collected by the data acquisition device 3 and stored as time-series data in the operation database 4. To identify which rotating machine 1 the vibration data belongs to, each rotating machine 1 is assigned an identification code, and the data for each rotating machine 1 can be searched, viewed, and retrieved using the identification code.

[0019] The operation database 4 can be either on-site or cloud-based. A cloud-based system is preferable because it allows for centralized management of multiple locations. Specifically, S. sensing (registered trademark) and Kurita-IT (registered trademark) can be applied.

[0020] The rotating equipment information database 5 stores information on the model, specifications, and performance of rotating equipment such as pumps and blowers that are subject to management, in a manner that associates them with identification codes that identify the rotating equipment.

[0021] The diagnostic device 6 is composed of a computer, for example, equipped with a CPU, a communication unit, a memory unit, etc. The memory unit includes, for example, RAM, ROM, a hard disk, etc. The functions of the diagnostic processing unit 7 are realized when the diagnostic program stored in the memory unit is executed.

[0022] The diagnostic device 6 extracts the data necessary for diagnosis from the rotating equipment information database 5 and the operation database 4, and performs the diagnosis in the diagnostic processing unit 7.

[0023] The diagnostic processing unit 7 reads and stores diagnostic models for each model type from the diagnostic model group database 10 and performs a diagnosis.

[0024] The diagnostic processing unit 7 has the function of extracting maintenance work for rotating equipment failures from the maintenance work history database 8 (described later), identifying the time and type of failure, and training the diagnostic model by machine learning the change patterns of diagnostic data before the failure occurred.

[0025] If an abnormality is detected as a result of the diagnosis, the person in charge can be notified by email or the result can be displayed on the screen (not shown in the diagram), although this is not illustrated. Rather than simply classifying the result as normal / abnormal (a binary value), the diagnosis result may be stored in the operation database 4 as numerical information ranging from 0 (normal) to 1 (abnormal), representing the degree of abnormality.

[0026] If maintenance work is performed based on the abnormality diagnosis results, the details of the maintenance work are stored in the maintenance work history database 8.

[0027] The maintenance work history database 8 records on-site maintenance and inspection records, as well as any issues such as detected anomalies, along with the date and time of their implementation or discovery. In particular, information on equipment failure detection and equipment maintenance work is stored in a way that allows it to be searched using the identification code of the relevant equipment.

[0028] The detailed procedure for anomaly diagnosis processing will be explained based on Figure 2.

[0029] In this abnormality diagnosis process, the identification code of one rotating piece of equipment is obtained from the list of equipment to be diagnosed (which may also be a list of identification codes of equipment to be diagnosed) 9 in the rotating equipment information database 5, and a diagnosis is performed (steps (1) to (7)). Once the diagnosis of all equipment included in list 9 has been completed, the entire diagnosis process is terminated (step (8)).

[0030] To perform the diagnosis, the rotating equipment identification code is used to obtain type information for the rotating equipment with that identification number from the rotating equipment information database 5 (step (3)). Performance information and specification information may also be obtained at this time.

[0031] Using the acquired model information, a diagnostic model is retrieved from the diagnostic model database 10, which stores diagnostic models for each model (step (4)). A diagnostic model includes data items (such as acceleration RMS and frequency), the time series data period, and a parameter array obtained when machine learning is performed, for the data used in the diagnosis.

[0032] The parameter array varies depending on the machine learning method used. For example, when diagnosing using a regression method with multiple vibration data, if the model formula is something like Anomaly Score = α × Acceleration RMS + β × Peak Frequency, then α and β would be stored as an array.

[0033] In another example, when using a method that inputs acceleration RMS and peak frequency data for the past 10 days into a neural network and outputs a confidence level for abnormality / normality, the parameter array corresponds to an array of weight data within the neural network.

[0034] Furthermore, since there are various machine learning methods, it is desirable to apply the method that has learned most accurately. Therefore, the data items and period input for each method should be variable to enable diagnosis.

[0035] Based on the acquired diagnostic model and the identification code of the rotating equipment, vibration data is extracted and obtained from the operation database (step (5)).

[0036] The acquired vibration data is input into the diagnostic model to obtain the diagnostic results (step (6)).

[0037] Perform maintenance work according to the diagnostic results (step (7)).

[0038] Once steps (1) through (7) above have been performed for all rotating equipment, the diagnosis for all rotating equipment is complete (step (8)).

[0039] To enable more effective use of the present invention, maintenance work information is stored in the maintenance work history information database 8.

[0040] The above embodiments are examples of the present invention, and the present invention is not limited thereto.

[0041] Although the present invention has been described in detail using specific embodiments, it will be apparent to those skilled in the art that various modifications are possible within the scope of achieving the effects of the invention. This application is based on Japanese Patent Application No. 2024-196093, filed on 8 November 2024, which is incorporated herein by reference in its entirety.

[0042] 1. Rotating machinery 2. Vibration sensor 3. Data acquisition device 4. Operation database 5. Rotating machinery information database 6. Diagnostic device 7. Diagnostic processing unit 8. Maintenance work history database 9. List of items to be diagnosed 10. Diagnostic model group database

Claims

1. A rotating equipment abnormality diagnostic device comprising: a vibration sensor for measuring vibrations of rotating equipment; an operation database for collecting and storing measurement data from the vibration sensor; a rotating equipment information database for recording at least the model, specifications, and performance information of the rotating equipment; a maintenance work history database for recording the maintenance work history of the rotating equipment; and a diagnostic processing unit for diagnosing abnormalities of the rotating equipment using the data from each of the above databases, wherein the diagnostic processing unit maintains a diagnostic model for each model and performs the diagnosis.

2. The rotating equipment abnormality diagnosis device according to claim 1, wherein each database is configured to identify individual rotating equipment using a common identification code.

3. The abnormality diagnosis device for rotating machinery according to claim 1 or 2, wherein the diagnostic processing unit has the function of extracting fault response maintenance work for rotating machinery from the maintenance work history database, identifying the time and type of fault, and training the diagnostic model by machine learning the change patterns of diagnostic data before the fault occurred.

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