Equipment fault early warning methods, devices, equipment and storage media

Abstract

This invention provides a method, apparatus, device, and storage medium for equipment fault early warning. The method includes: collecting real-time vibration data of the equipment under test; triggering an equipment fault early warning notification when the real-time vibration data meets preset early warning conditions; wherein the tolerance parameter of the early warning conditions is determined based on historical vibration data of the equipment under test under normal operating conditions. This invention can provide early warning of equipment faults before they occur, enabling maintenance personnel to promptly inspect and repair the equipment, thus avoiding losses caused by unplanned downtime due to equipment failure.

Description

Technical Field

[0001] This invention relates to the field of data monitoring technology, and in particular to a method, apparatus, device, and storage medium for early warning of equipment failure. Background Technology

[0002] In the technology of mechanical equipment fault monitoring, faults are mainly detected through two methods: one is to develop a regular inspection plan and conduct inspections regularly; the other is to obtain equipment status and fault parameters through an equipment monitoring system. Equipment faults detected through these two methods usually occur after the fact, meaning that the fault can only be detected after it has happened, causing unplanned downtime and losses for the company. Summary of the Invention

[0003] The present invention aims to provide a method, apparatus, device and storage medium for early warning of equipment failure, so as to solve the above-mentioned technical problems, and to provide early warning before equipment failure, thereby avoiding losses suffered by enterprises due to unplanned downtime caused by equipment failure.

[0004] To address the aforementioned technical problems, this invention provides a method for early warning of equipment failure, comprising:

[0005] Collect real-time vibration data of the device under test;

[0006] When the real-time vibration data is determined to meet the preset warning conditions, a device fault warning notification is triggered; wherein, the tolerance parameter of the warning conditions is determined based on the historical vibration data of the device under test under normal operating conditions.

[0007] Furthermore, determining that the real-time vibration data meets the preset early warning conditions includes:

[0008] When the real-time vibration data exceeds the data anomaly threshold limited by the tolerance parameter of the warning condition, an equipment vibration anomaly is recorded.

[0009] When the cumulative number of recorded abnormal equipment vibrations exceeds the first threshold corresponding to the data anomaly threshold, the real-time vibration data is determined to meet the warning condition.

[0010] Furthermore, determining that the real-time vibration data meets the preset early warning conditions also includes:

[0011] When the cumulative number of abnormal equipment vibrations recorded within a preset time period exceeds the second threshold corresponding to the data anomaly threshold, the real-time vibration data is determined to meet the warning condition.

[0012] Furthermore, the real-time vibration data of the device under test is acquired using a triaxial sensor; the step of triggering a device fault warning notification when the real-time vibration data meets preset warning conditions includes:

[0013] The real-time vibration data is divided into multiple dimensions to determine whether preset warning conditions are met. When the real-time vibration data of any dimension meets the preset warning conditions, a device fault warning notification is triggered.

[0014] Furthermore, the method for determining the tolerance parameter of the early warning condition includes:

[0015] Acquire historical vibration data of the device under test under normal operating conditions, and determine the maximum, minimum, and average values ​​of the historical vibration data;

[0016] The tolerance parameters for the early warning conditions are determined based on the maximum, minimum, and average values ​​of the historical vibration data.

[0017] Furthermore, the device fault warning notification includes at least one of the following methods: system notification, APP push notification, and SMS notification.

[0018] Furthermore, following the triggering of the device fault warning notification, the following is also included:

[0019] The system determines the anomaly type corresponding to the real-time vibration data when it meets preset warning conditions, and generates a maintenance work order corresponding to the anomaly type.

[0020] The present invention also provides a device for early warning of equipment failure, comprising:

[0021] The data acquisition module is used to collect real-time vibration data of the device under test.

[0022] The fault warning module is used to trigger a device fault warning notification when the real-time vibration data is determined to meet the preset warning conditions; wherein the tolerance parameter of the warning conditions is determined based on the historical vibration data of the device under test under normal operating conditions.

[0023] The present invention also provides a terminal device, including a processor and a memory storing a computer program, wherein the processor executes the computer program to implement any of the device fault early warning methods described herein.

[0024] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the device fault early warning methods described herein.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] This invention provides a method, apparatus, device, and storage medium for equipment fault early warning. The method includes: collecting real-time vibration data of the device under test; triggering an equipment fault early warning notification when the real-time vibration data meets preset early warning conditions; wherein the tolerance parameter of the early warning conditions is determined based on historical vibration data of the device under test under normal operating conditions. This invention can provide early warning of faults before equipment failure occurs, enabling maintenance personnel to promptly inspect and repair the equipment, thus avoiding losses caused by unplanned downtime due to equipment failure. Attached Figure Description

[0027] Figure 1 This is one of the flowcharts of the equipment fault early warning method provided by the present invention;

[0028] Figure 2 This is the second flowchart of the equipment fault early warning method provided by the present invention;

[0029] Figure 3 This is a schematic diagram of the equipment fault early warning device provided by the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Please see Figure 1 This invention provides a method for early warning of equipment failure, which may include the following steps:

[0032] S1. Collect real-time vibration data of the device under test;

[0033] S2. When it is determined that the real-time vibration data meets the preset early warning conditions, a device fault early warning notification is triggered; wherein, the tolerance parameter of the early warning conditions is determined based on the historical vibration data of the device under test under normal operating conditions.

[0034] It should be noted that tolerance parameters for early warning conditions can be pre-set based on historical vibration data of the equipment under test during normal operation. These early warning conditions can include various anomaly types, providing early warnings from multiple perspectives and thus improving the accuracy of the warnings. When the real-time vibration data meets the set early warning conditions based on the tolerance parameters, a device fault warning notification is triggered, allowing maintenance personnel to promptly detect equipment anomalies and formulate corresponding maintenance and repair plans in advance. Because maintenance personnel are alerted to perform inspections and maintenance before equipment failure occurs, losses suffered by the company due to unplanned downtime caused by equipment failure are effectively avoided.

[0035] In this embodiment of the invention, further, determining that the real-time vibration data meets the preset early warning conditions includes:

[0036] When the real-time vibration data exceeds the data anomaly threshold limited by the tolerance parameter of the warning condition, an equipment vibration anomaly is recorded.

[0037] When the cumulative number of recorded abnormal equipment vibrations exceeds the first threshold corresponding to the data anomaly threshold, the real-time vibration data is determined to meet the warning condition.

[0038] It should be noted that multiple data anomaly thresholds can be set, each corresponding to a permissible number of anomalies. Each time real-time vibration data exceeds a data anomaly threshold, a vibration anomaly is recorded. When the cumulative number of recorded vibration anomalies exceeds the corresponding threshold, a fault warning is issued.

[0039] In this embodiment of the invention, the step of determining that the real-time vibration data meets the preset early warning conditions further includes:

[0040] When the cumulative number of abnormal equipment vibrations recorded within a preset time period exceeds the second threshold corresponding to the data anomaly threshold, the real-time vibration data is determined to meet the warning condition.

[0041] It should be noted that, regarding the determination of the number of abnormal equipment vibrations, the cumulative recorded number of times in the previous embodiment was independent of time; an alert was triggered when the cumulative number reached a certain threshold. Further, in this embodiment of the invention, the alert can be triggered when the corresponding threshold is reached within a certain time range, for example, when the number of abnormal equipment vibrations reaches 3 times within a week. It should also be noted that the cumulative recorded number of abnormal equipment vibrations can be reset to zero after maintenance personnel perform repairs or servicing.

[0042] In this embodiment of the invention, the real-time vibration data of the device under test is further collected using a triaxial sensor; the step of triggering a device fault warning notification when the real-time vibration data meets preset warning conditions includes:

[0043] The real-time vibration data is divided into multiple dimensions to determine whether preset warning conditions are met. When the real-time vibration data of any dimension meets the preset warning conditions, a device fault warning notification is triggered.

[0044] It should be noted that real-time vibration data can be divided into multiple dimensions, and warning conditions can be determined for each dimension. When the real-time vibration data in any dimension meets the preset warning conditions, a device fault warning notification is triggered. This approach enables more refined data monitoring and further improves the accuracy of device data warnings.

[0045] In this embodiment of the invention, the method for determining the tolerance parameter of the early warning condition further includes:

[0046] Acquire historical vibration data of the device under test under normal operating conditions, and determine the maximum, minimum, and average values ​​of the historical vibration data;

[0047] The tolerance parameters for the early warning conditions are determined based on the maximum, minimum, and average values ​​of the historical vibration data.

[0048] It should be noted that, based on the historical vibration data of the device under test under normal operating conditions, the maximum, minimum and average values ​​of the data in each dimension can be obtained. Based on the degree of deviation of the maximum and minimum values ​​from the average value, the corresponding data tolerance value can be determined as the critical value for determining whether the warning conditions are met.

[0049] In this embodiment of the invention, the device fault warning notification further includes at least one of the following: system notification method, APP push notification method, and SMS notification method.

[0050] It should be noted that multiple notification methods can be set, and different notification methods can be triggered according to different types of device malfunctions.

[0051] In this embodiment of the invention, after triggering the device fault warning notification, the method further includes:

[0052] The system determines the anomaly type corresponding to the real-time vibration data when it meets preset warning conditions, and generates a maintenance work order corresponding to the anomaly type.

[0053] It should be noted that after a device fault warning is triggered, a corresponding maintenance work order can be generated based on the current anomaly type and sent to the corresponding maintenance personnel's terminal, so that the corresponding maintenance personnel can respond to the maintenance work order and perform maintenance operations on the device.

[0054] Based on the above solution, and to facilitate a better understanding of the equipment fault early warning method provided in the embodiments of the present invention, the following detailed description is provided:

[0055] To address the technical problems raised in the background section, the purpose of this invention is to perform predictive maintenance monitoring of machine tool equipment. By analyzing monitoring data in real time, it can detect equipment problems in advance and issue early warnings, enabling maintenance personnel to conduct timely inspections and repairs. The main implementation methods include:

[0056] 1. Acquisition of normal equipment vibration data: By installing triaxial vibration sensors, normal vibration data of the equipment is collected and transmitted to the monitoring platform for storage.

[0057] 2. Generate healthy vibration data for equipment: By analyzing normal vibration data, a healthy vibration data model is generated.

[0058] 3. Judgment and early warning of abnormal equipment vibration data: By comparing real-time vibration data with healthy vibration data models, early warnings are issued for abnormal data that exceeds the tolerance range according to user definitions.

[0059] Please see Figure 2 The embodiments of the present invention can be implemented through the following steps:

[0060] 1. Identify the monitoring targets:

[0061] Identify the equipment whose vibration needs to be monitored.

[0062] 2. Install the data acquisition sensors:

[0063] The sensors should be installed according to the specific conditions of the equipment. The installation principle is not to affect the operation of the machine or damage its structure. Installation methods include drilling, pasting, etc.

[0064] 3. Information Collection:

[0065] Three-axis sensors (XYZ axes) are used to collect three-axis vibration data during normal operation of the equipment. The data is transmitted to a communication gateway via communication interfaces (including but not limited to RS232 and RS485 interfaces), and the communication gateway uploads the data to the IoT platform. To establish a reliable health data model, it is usually necessary to collect data for more than three days (data is collected at preset intervals).

[0066] 4. Generate equipment health data:

[0067] Each time the sensor collects XYZ three-axis data, it stores the data in different fields of the database and analyzes the maximum, minimum and average values ​​of each dimension as the basis for setting the amplitude range when the device is working properly.

[0068] 5. Early Warning Definition:

[0069] The platform allows users to set real-time monitoring data alarm tolerance, which data dimension to monitor, how many times data anomalies will trigger an alert, how many times data anomalies will trigger an alert within a certain period, alert methods (including system notifications, WeChat, SMS, etc.), and whether to generate work orders.

[0070] 6. Real-time equipment status monitoring:

[0071] The sensors monitor the equipment status in real time. When the difference between the collected data and the health and working data exceeds the defined range of the previous step, the system automatically triggers an early warning.

[0072] 7. Generate repair / warranty work orders:

[0073] The system can interface with the "Equipment Management System (or any related business system)" and automatically generate a "Maintenance / Repair Work Order" in the Equipment Management System (or any related business system) when an alert is issued.

[0074] It should be noted that the features of the non-inventive embodiments include:

[0075] 1. Monitor equipment health through predictive maintenance to provide early warnings of equipment problems.

[0076] 2. Generate equipment health data: The XYZ axis data collected by the sensors are stored in different fields of the database to form the range of equipment health data.

[0077] 3. Customizable device anomaly alerts: The scope and method of alerts can be defined in detail, including alarm tolerance, monitoring dimensions, alert conditions, and alert methods.

[0078] 4. Automatically generates repair / warranty work orders: It can interface with equipment management and other systems to automatically generate repair / maintenance work orders.

[0079] It should be noted that, for the sake of simplicity, the above methods or process embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0080] Please see Figure 3 This invention also provides a device for early warning of equipment failure, comprising:

[0081] Data acquisition module 1 is used to acquire real-time vibration data of the device under test;

[0082] The fault warning module 2 is used to trigger a device fault warning notification when the real-time vibration data is determined to meet the preset warning conditions; wherein the tolerance parameter of the warning conditions is determined based on the historical vibration data of the device under test under normal operating conditions.

[0083] Furthermore, the fault early warning module 2 is specifically used for:

[0084] When the real-time vibration data exceeds the data anomaly threshold limited by the tolerance parameter of the warning condition, an equipment vibration anomaly is recorded.

[0085] When the cumulative number of recorded abnormal equipment vibrations exceeds the first threshold corresponding to the data anomaly threshold, the real-time vibration data is determined to meet the warning condition.

[0086] Furthermore, the fault early warning module 2 is specifically used for:

[0087] When the cumulative number of abnormal equipment vibrations recorded within a preset time period exceeds the second threshold corresponding to the data anomaly threshold, the real-time vibration data is determined to meet the warning condition.

[0088] Furthermore, the real-time vibration data of the device under test is acquired using a triaxial sensor; the fault early warning module 2 is specifically used for:

[0089] The real-time vibration data is divided into multiple dimensions to determine whether preset warning conditions are met. When the real-time vibration data of any dimension meets the preset warning conditions, a device fault warning notification is triggered.

[0090] Furthermore, the method for determining the tolerance parameter of the early warning condition includes:

[0091] Acquire historical vibration data of the device under test under normal operating conditions, and determine the maximum, minimum, and average values ​​of the historical vibration data;

[0092] The tolerance parameters for the early warning conditions are determined based on the maximum, minimum, and average values ​​of the historical vibration data.

[0093] Furthermore, the device fault warning notification includes at least one of the following methods: system notification, APP push notification, and SMS notification.

[0094] Furthermore, the equipment fault early warning device also includes a work order generation module, which is used for:

[0095] The system determines the anomaly type corresponding to the real-time vibration data when it meets preset warning conditions, and generates a maintenance work order corresponding to the anomaly type.

[0096] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention. The equipment fault early warning device provided by the embodiments of the present invention can implement the equipment fault early warning method provided by any one of the method embodiments of the present invention.

[0097] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the device fault early warning methods described herein.

[0098] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0099] Those skilled in the art will clearly understand that, for convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0100] The terminal device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0101] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0102] The memory can be used to store the computer program. The processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0103] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0104] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for early warning of equipment failure, characterized in that, include: Collect real-time vibration data of the device under test; When the real-time vibration data is determined to meet a preset warning condition, a device fault warning notification is triggered. The determination that the real-time vibration data meets the preset warning condition includes: recording a device vibration anomaly when the real-time vibration data exceeds a data anomaly threshold defined by the tolerance parameter of the warning condition; determining that the real-time vibration data meets the warning condition when the cumulative number of recorded device vibration anomalies is greater than the first threshold corresponding to the data anomaly threshold; the tolerance parameter of the warning condition is determined based on historical vibration data of the device under test under normal operating conditions, specifically: acquiring historical vibration data of the device under test under normal operating conditions, determining the maximum, minimum, and average values ​​of the historical vibration data; and determining the tolerance parameter of the warning condition based on the degree of deviation of the maximum and minimum values ​​of the historical vibration data from the average value.

2. The equipment fault early warning method according to claim 1, characterized in that, The determination that the real-time vibration data meets the preset early warning conditions also includes: When the cumulative number of abnormal equipment vibrations recorded within a preset time period exceeds the second threshold corresponding to the data anomaly threshold, the real-time vibration data is determined to meet the warning condition.

3. The equipment fault early warning method according to claim 1, characterized in that, The real-time vibration data of the device under test is collected using a triaxial sensor; When the real-time vibration data is determined to meet preset early warning conditions, a device fault early warning notification is triggered, including: The real-time vibration data is divided into multiple dimensions to determine whether preset warning conditions are met. When the real-time vibration data of any dimension meets the preset warning conditions, a device fault warning notification is triggered.

4. The equipment fault early warning method according to claim 1, characterized in that, The device fault warning notification includes at least one of the following methods: system notification, APP push notification, and SMS notification.

5. The equipment fault early warning method according to any one of claims 1-4, characterized in that, Following the triggering of the device fault warning notification, the following is also included: The system determines the anomaly type corresponding to the real-time vibration data when it meets preset warning conditions, and generates a maintenance work order corresponding to the anomaly type.

6. A device for early warning of equipment failure, characterized in that, include: The data acquisition module is used to collect real-time vibration data of the device under test. A fault early warning module is used to trigger a device fault early warning notification when the real-time vibration data is determined to meet a preset early warning condition. The determination that the real-time vibration data meets the preset early warning condition includes: recording a device vibration anomaly when the real-time vibration data exceeds a data anomaly threshold defined by the tolerance parameter of the early warning condition; determining that the real-time vibration data meets the early warning condition when the cumulative number of recorded device vibration anomalies is greater than the first threshold corresponding to the data anomaly threshold; the tolerance parameter of the early warning condition is determined based on historical vibration data of the device under test under normal operating conditions, specifically: acquiring historical vibration data of the device under test under normal operating conditions, determining the maximum, minimum, and average values ​​of the historical vibration data; and determining the tolerance parameter of the early warning condition based on the degree of deviation of the maximum and minimum values ​​of the historical vibration data from the average value.

7. A terminal device, comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the device fault early warning method according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the device fault early warning method as described in any one of claims 1 to 5.

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