Automatic fault diagnosis and alarm processing method for dust management and control integrated platform

Abstract

The invention relates to the technical field of environment monitoring and automatic control technologies, and particularly discloses an automatic fault diagnosis and alarm processing method for a dust management and control integrated platform, which comprises the following steps of: 1, acquiring dust concentration time sequence data C (t) and a current characteristic I (t) of associated equipment; 2, when C (t) exceeds a set threshold value, establishing a fault decision-making tree containing equipment states and environmental parameters; 3, dynamically adjusting the power of the dust removal equipment and the starting strategy of the emergency device according to the fault level; and step 4, a grading alarm signal is sent to a central control room according to the fault grade, and a maintenance work order is generated. The method has the beneficial effects that the false alarm rate is remarkably reduced through multi-sensor data fusion and equipment state linkage judgment; the fault type can be accurately positioned through a grading alarm mechanism, and energy consumption waste caused by blindly increasing the equipment load is avoided; and the voice alarm and forced spraying functions ensure the emergency response timeliness, and dust diffusion is effectively restrained.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring and automatic control technology, and in particular to a method for automatic fault diagnosis and alarm processing of an integrated dust control platform. Background Art

[0002] Existing dust control systems are widely used in industrial environments, but they generally have problems with imperfect fault diagnosis and alarm mechanisms. Traditional systems mostly rely on single sensor threshold alarms, which are prone to false alarms or missed alarms due to environmental interference or sensor drift. For example, when the dust concentration exceeds the limit but the spray valve does not start due to mechanical failure, the system may only record the data and fail to trigger emergency measures in time, resulting in continued dust spread. In addition, the existing alarm function lacks a classification strategy and cannot dynamically adjust the response measures according to the severity of the fault, resulting in equipment overload or increased energy consumption. Although some systems have remote monitoring functions, their data integration capabilities are insufficient, making it difficult to achieve coordinated control of multiple devices. Therefore, there is an urgent need for a solution that can diagnose faults in real time, intelligently classify alarms, and link control equipment to improve system reliability and safety. Summary of the Invention

[0003] In order to solve the above problems, the present invention proposes a method for automatic fault diagnosis and alarm processing of an integrated dust control platform.

[0004] The technical solution of the present invention is achieved as follows:

[0005] A method for automatic fault diagnosis and alarm processing of an integrated dust control platform includes the following steps:

[0006] Step 1: Obtain dust concentration time series data C(t) and current characteristics I(t) of associated equipment;

[0007] Step 2: When C(t) exceeds the set threshold, a fault decision tree including equipment status and environmental parameters is established;

[0008] Step 3: Dynamically adjust the power of the dust removal equipment and the activation strategy of the emergency device according to the fault level;

[0009] Step 4: Send a graded alarm signal to the central control room based on the fault level and generate a maintenance work order.

[0010] Furthermore, the fault level in step 2 is divided into three levels: level 1 is device failure, level 2 is insufficient load, and level 3 is system overload. The fault decision tree includes:

[0011] Level 1 fault judgment condition: I(t) < 15% of rated current and valve opening θ(t) = 0;

[0012] Secondary fault judgment condition: I(t) ∈ [15%, 80%] of the rated current and ΔC / Δt < 0.2 mg / (m 3 ·s);

[0013] Tertiary fault judgment condition: I(t) > 120% of the rated current for 30 seconds.

[0014] Furthermore, the dynamic adjustment in step 3 includes:[[]]

[0015] If it is a primary fault, trigger a voice alarm and forcibly activate the standby sprinkler device;

[0016] If it is a secondary fault, perform ramp-up speed control on the variable-frequency fan with a speed-up rate of 0.5 Hz / s; use the PID control algorithm to adjust the solenoid valve opening of the sprinkler device, with the integral time constant Ti = 25 s, and the number of activated sprinkler heads gradually increases at two-minute intervals;

[0017] If it is a tertiary fault, cut off the main circuit and start the inert gas explosion suppression system, and send the highest-priority alarm to the central control room.

[0018] Furthermore, in step 4, the maintenance work order is automatically dispatched to the designated maintenance personnel terminal through the cloud server.

[0019] Furthermore, the automatic dispatch is performed according to the work order priority. The work order priority weight calculation model: Priority = 0.6 × fault level + 0.3 × multiple of concentration exceeding the standard + 0.1 × equipment criticality.

[0020] By adopting the above technical solutions, the beneficial effects of the present invention are as follows: Through the multi-sensor data fusion and the linkage judgment of the equipment status, the false alarm rate is significantly reduced; the hierarchical alarm mechanism can accurately locate the fault type and avoid the energy consumption waste caused by blindly increasing the equipment load; the voice alarm and the forced sprinkler function ensure the timeliness of the emergency response and effectively contain the dust diffusion; the cloud work order management improves the maintenance efficiency and reduces the downtime. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 It is a schematic diagram of the system topology structure of the present invention;

[0023] Figure 2 It is a flowchart of the multi-sensor data fusion of the present invention;

[0024] Figure 3 is the state transition diagram of hierarchical alarm of the present invention. Specific embodiments

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work shall fall within the protection scope of the present invention.

[0026] The dust control integrated platform of the enterprise includes:

[0027] Data perception layer: explosion-proof laser scattering dust sensor (range 0-1000mg / m 3 , accuracy ±3% FS), infrared gas detection array, distributed temperature and humidity sensor;

[0028] Edge computing layer: embedded industrial computer (equipped with multi-source data fusion algorithm);

[0029] Execution control layer: variable frequency dust removal fan (power range 5.5-75kW), spray device controlled by solenoid valve and standby spray device, explosion-proof directional sound and light alarm;

[0030] Cloud service platform: maintenance work order management system based on microservices.

[0031] As Figures 1 - 3 shown, a method for automatic fault diagnosis and alarm processing of a dust control integrated platform includes the following steps:

[0032] Step 1: Multimodal data acquisition

[0033] Synchronously collect dust concentration C(t), device current I(t), valve opening θ(t), ambient temperature and humidity (T, H);

[0034] Use the sliding window filtering algorithm to process the original data, and the window length Δt = 30s;

[0035] Step 2: Fault diagnosis decision tree

[0036] When C(t) ≥ threshold C_th:

[0037] a) Check the status of associated devices: If I(t) < no-load current threshold I_min and θ(t) = 0, it is determined as a first-level fault (device not responding);

[0038] b) If I(t) ∈ [I_min, I_max] and the decreasing rate of C(t), ΔC / Δt < the set value, it is determined as a secondary fault (insufficient load);

[0039] c) If I(t) > I_max and C(t) continues to rise, it is determined as a tertiary fault (system overload);

[0040] Step 3: Hierarchical response strategy

[0041] Primary fault: Trigger area - directed broadcast (sound pressure level ≥ 85 dB@1m), start the standby sprinkler device (open 30% of the nozzles in the first stage, increase by 20% every 120 s until the concentration drops);

[0042] Secondary fault: Gradually increase the fan speed by gradient (increase 5 Hz per gear, with an interval of 60 s), and at the same time activate the vibration monitoring module; Use the PID control algorithm to adjust the solenoid valve opening of the sprinkler device, with the integral time constant Ti = 25 s, and the number of sprinkler heads put into use gradually increases at two - minute intervals;

[0043] Tertiary fault: Cut off the main circuit and start the inert gas explosion suppression system, send the highest - priority alarm to the central control room;

[0044] Step 4: Cloud collaborative management

[0045] Generate an electronic work order containing the fault code, on - site video snapshot, and maintenance history record.

[0046] Preferred embodiment:

[0047] In the application in the coal mine crushing workshop:

[0048] Set C_th = 8 mg / m 3 (GBZ2.1 standard). When it is detected that C(t) = 12 mg / m 3 and the current of the dust removal fan of the crusher is zero, start the 3 sprinkler units closest to the fault point (total flow Q = 15 m 3 / h), send an alarm with code F001 to the on - well central control through the LoRa wireless module, and automatically retrieve the last 3 maintenance records of this fan for the maintenance personnel to refer to.

[0049] The PID control algorithm in this application is composed of Proportional, Integral, and Differential. PID regulation is the most mature and widely used regulation method in continuous control systems. The essence of PID regulation is to perform operations according to the input deviation value according to the functional relationships of proportion, integral, and differential, and the operation results are used to control the output. The PID algorithm has a history of more than 100 years and is applied in scenarios such as quadcopters, balance cars, automotive cruise control, and temperature controllers. Therefore, it will not be elaborated in detail.

[0050] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for automatic fault diagnosis and alarm handling of an integrated platform for dust control and management, characterized in that: It includes the following steps: Step 1: Obtain the time-series data of dust concentration C(t) and the current characteristics I(t) of associated equipment; Step 2: When C(t) exceeds the set threshold, establish a fault decision tree including equipment status and environmental parameters; Step 3: Dynamically adjust the power of the dust removal equipment and the start-up strategy of the emergency device according to the fault level; Step 4: Send a classified alarm signal to the central control room according to the fault level and generate a maintenance work order.

2. The automatic fault diagnosis and alarm processing method for an integrated dust control platform according to claim 1, characterized in that: The fault levels in Step 2 are divided into three levels: Level 1 is that the equipment does not respond, Level 2 is insufficient load, and Level 3 is system overload. The fault decision tree includes: Judgment condition for Level 1 fault: I(t) < 15% of the rated current and the valve opening θ(t) = 0; Secondary fault judgment condition: I(t) ∈ [15%, 80%] of the rated current and ΔC / Δt < 0.2 mg / (m 3 ·s); Judgment condition for Level 3 fault: I(t) > 120% of the rated current for 30 seconds.

3. The automatic fault diagnosis and alarm handling method for an integrated dust control platform according to claim 2, characterized in that: The dynamic adjustment in Step 3 includes: If it is a Level 1 fault, trigger a voice alarm and forcibly turn on the standby spray device; If it is a Level 2 fault, perform ramp-up speed control on the variable-frequency fan with a ramp-up rate of 0.5 Hz / s; use the PID control algorithm to adjust the solenoid valve opening of the spray device, with an integral time constant Ti = 25 s, and the number of nozzles put into use increases step by step at two-minute intervals; If it is a Level 3 fault, cut off the main circuit and start the inert gas explosion suppression system, and send the highest-priority alarm to the central control room.

4. The automatic fault diagnosis and alarm handling method for an integrated dust control platform according to claim 1, characterized in that: In Step 4, the maintenance work order is automatically dispatched to the designated maintenance personnel terminal through the cloud server.

5. The automatic fault diagnosis and alarm handling method for an integrated dust control platform according to claim 4, characterized in that: Automatically dispatch according to the work order priority. The work order priority weight calculation model: Priority = 0.6 × fault level + 0.3 × multiple of concentration exceeding the standard + 0.1 × equipment criticality.

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