CN110345463A - A kind of boiler incipient fault recognition methods and device - Google Patents
A kind of boiler incipient fault recognition methods and device Download PDFInfo
- Publication number
- CN110345463A CN110345463A CN201910547774.9A CN201910547774A CN110345463A CN 110345463 A CN110345463 A CN 110345463A CN 201910547774 A CN201910547774 A CN 201910547774A CN 110345463 A CN110345463 A CN 110345463A
- Authority
- CN
- China
- Prior art keywords
- boiler
- operating parameter
- early warning
- module
- classifier
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 15
- 238000003066 decision tree Methods 0.000 claims abstract description 24
- 238000012549 training Methods 0.000 claims abstract description 16
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 238000002485 combustion reaction Methods 0.000 claims description 14
- 238000003062 neural network model Methods 0.000 claims description 13
- 230000005856 abnormality Effects 0.000 abstract 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 7
- 238000010438 heat treatment Methods 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000003546 flue gas Substances 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 239000002918 waste heat Substances 0.000 description 1
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F22—STEAM GENERATION
- F22B—METHODS OF STEAM GENERATION; STEAM BOILERS
- F22B35/00—Control systems for steam boilers
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Physics & Mathematics (AREA)
- Thermal Sciences (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Control Of Steam Boilers And Waste-Gas Boilers (AREA)
- Regulation And Control Of Combustion (AREA)
Abstract
本发明公开了一种锅炉潜在故障识别方法以及识别装置，识别方法包括初始化并完成决策树分类器的训练操作；实时采集锅炉的运行参数；计算各个运行参数的变化趋势；计算各个运行参数及其对应的变化率值的预警范围；判断各个运行参数是否在预警范围内；将各个运行参数以及所对应的变化率值组成一个待测样本数据；将所述待测样本数据输入到策树分类器中，所述决策树分类器输出锅炉的潜在故障类型。本发明通过所采集的锅炉的实时运行参数，计算各个运行参数的变化率值，同时根据运行参数及其变化率值，首先判断其数据本身是否存在异常，最后利用决策分类器判断锅炉运行过程中的潜在故障，有效降低锅炉运行过程中的故障风险。
The invention discloses a boiler potential fault identification method and an identification device. The identification method includes initialization and completion of the training operation of a decision tree classifier; real-time collection of operating parameters of the boiler; calculation of the change trend of each operating parameter; calculation of each operating parameter and its The early warning range of the corresponding rate of change value; judging whether each operating parameter is within the early warning range; forming a sample data to be tested by each operating parameter and the corresponding rate of change value; inputting the sample data to be tested into the strategy tree classifier In , the decision tree classifier outputs the potential failure types of the boiler. The present invention calculates the rate of change of each operating parameter through the collected real-time operating parameters of the boiler, and at the same time, according to the operating parameters and their rate of change, first judges whether there is any abnormality in the data itself, and finally uses a decision-making classifier to judge whether the boiler is in operation. potential failures, effectively reducing the risk of failures during boiler operation.
Description
技术领域technical field
本发明涉及智能识别技术领域，更具体地说涉及一种锅炉潜在故障识别方法以及装置。The invention relates to the technical field of intelligent identification, and more specifically relates to a method and a device for identifying a potential failure of a boiler.
背景技术Background technique
锅炉是一种日常生活中常见的能量转换装置，其适用于生活中的方方面面，例如供暖、发电等。锅炉是一种利用燃料燃烧后释放的热能或工业生产中的余热传递给容器内的水，使水达到所需要的温度或一定压力蒸汽的热力设备。锅炉在“锅”与“炉”两部分同时进行，水进入锅炉以后，在汽水系统中锅炉受热面将吸收的热量传递给水，使水加热成一定温度和压力的热水或生成蒸汽，被引出应用。在燃烧设备部分，燃料燃烧不断放出热量，燃烧产生的高温烟气通过热的传播，将热量传递给锅炉受热面，而本身温度逐渐降低，最后由烟囱排出。A boiler is a common energy conversion device in daily life, which is applicable to all aspects of life, such as heating, power generation, etc. A boiler is a thermal device that uses the heat energy released after fuel combustion or the waste heat in industrial production to transfer to the water in the container to make the water reach the required temperature or steam at a certain pressure. The boiler is operated in two parts of the "pot" and "furnace" at the same time. After the water enters the boiler, the heating surface of the boiler in the steam-water system transfers the absorbed heat to the water, so that the water is heated into hot water at a certain temperature and pressure or steam is drawn out. application. In the part of the combustion equipment, the combustion of the fuel continuously releases heat. The high-temperature flue gas generated by the combustion transfers the heat to the heating surface of the boiler through heat transmission, and the temperature itself gradually decreases, and finally it is discharged from the chimney.
现有的锅炉装置普遍通过多个安装在锅炉不同位置上的传感器以及检测锅炉的实时运行参数，并通过锅炉的运行参数实时地检测锅炉是否出现故障，虽然这种检测方案尽管能够及时地检测出故障，但是故障被检测出来时也就意味着故障已经发生了，因此或多或少都会造成设备的损坏，从而造成经济损失。即现有的锅炉装置并没有配置相应的，对其潜在故障进行识别预测的功能，导致现有的锅炉装置出现故障风险较高。Existing boiler devices generally use multiple sensors installed in different positions of the boiler to detect the real-time operating parameters of the boiler, and detect whether the boiler is faulty in real time through the operating parameters of the boiler, although this detection scheme can detect in time Fault, but when the fault is detected, it means that the fault has occurred, so more or less it will cause damage to the equipment, resulting in economic losses. That is, the existing boiler equipment is not equipped with the corresponding function of identifying and predicting potential failures, resulting in a high risk of failure of the existing boiler equipment.
发明内容Contents of the invention
本发明要解决的技术问题是：提供一种锅炉潜在故障识别方法以及装置，主要是针对发电用锅炉系统。The technical problem to be solved by the present invention is to provide a boiler potential fault identification method and device, mainly for boiler systems for power generation.
本发明解决其技术问题的解决方案是：The solution that the present invention solves its technical problem is:
一种锅炉潜在故障识别方法，包括以下步骤：A boiler potential fault identification method, comprising the following steps:
步骤100，初始化决策树分类器，输入多组训练样本数据，完成决策树分类器的训练操作；Step 100, initialize the decision tree classifier, input multiple sets of training sample data, and complete the training operation of the decision tree classifier;
步骤200，实时采集锅炉的多个运行参数；Step 200, collecting multiple operating parameters of the boiler in real time;
步骤300，分别计算各个运行参数的变化趋势，得到各个运行参数所对应的变化率值；Step 300, respectively calculate the change trend of each operating parameter, and obtain the change rate value corresponding to each operating parameter;
步骤400，根据输入的控制参数，分别计算各个运行参数及其对应的变化率值的预警范围；Step 400, according to the input control parameters, respectively calculate the early warning ranges of each operating parameter and its corresponding change rate value;
步骤500，分别判断各个运行参数是否在预警范围内，如果是，输出预警信号；Step 500, respectively judge whether each operating parameter is within the warning range, if yes, output a warning signal;
步骤600，将各个运行参数以及所对应的变化率值组成一个待测样本数据；Step 600, each operating parameter and the corresponding change rate value form a sample data to be tested;
步骤700，将所述待测样本数据输入到策树分类器中，所述决策树分类器输出锅炉的潜在故障类型。Step 700, input the sample data to be tested into a decision tree classifier, and the decision tree classifier outputs the potential failure type of the boiler.
作为上述技术方案的进一步改进，将步骤700替代为步骤800，设置贝叶斯分类器，将所述待测样本数据输入到贝叶斯分类器中，所述贝叶斯分类器输出锅炉的潜在故障类型的发生概率。As a further improvement of the above technical solution, step 700 is replaced by step 800, a Bayesian classifier is set, the sample data to be tested is input into the Bayesian classifier, and the Bayesian classifier outputs the potential of the boiler The probability of occurrence of the type of failure.
作为上述技术方案的进一步改进，所述运行参数包括炉内温度、炉内气压、燃烧室温度、燃烧室气压、送风机转速、发电机转速以及输出电参数。As a further improvement of the above technical solution, the operating parameters include furnace temperature, furnace air pressure, combustion chamber temperature, combustion chamber air pressure, blower speed, generator speed and output electrical parameters.
作为上述技术方案的进一步改进，步骤400中包括初始化并训练神经网络模型，根据输入的控制参数，所述神经网络模型分别计算各个运行参数及其对应的变化率值的预警范围。As a further improvement of the above technical solution, step 400 includes initializing and training the neural network model, and according to the input control parameters, the neural network model calculates the early warning ranges of each operating parameter and its corresponding change rate value.
本发明同时公开了一种锅炉潜在故障识别装置，包括：The invention also discloses a boiler potential fault identification device, comprising:
决策树分类器生成模块，用于初始化决策树分类器，并且完成决策树分类器的训练操作；Decision tree classifier generation module, used to initialize the decision tree classifier, and complete the training operation of the decision tree classifier;
传感器模块，用于实时采集锅炉的多个运行参数；The sensor module is used to collect multiple operating parameters of the boiler in real time;
第一计算模块，用于分别计算各个运行参数的变化趋势，得到各个运行参数所对应的变化率值；The first calculation module is used to separately calculate the change trend of each operating parameter, and obtain the change rate value corresponding to each operating parameter;
第二计算模块，用于根据输入的控制参数，分别计算各个运行参数及其对应的变化率值的预警范围；The second calculation module is used to calculate the early warning ranges of each operating parameter and its corresponding change rate value according to the input control parameters;
判断模块，用于分别判断各个运行参数是否在预警范围内，如果是，输出预警信号；A judging module, used to judge whether each operating parameter is within the early warning range, and if so, output an early warning signal;
样本生成模块，用于将各个运行参数以及所对应的变化率值组成一个待测样本数据；The sample generation module is used to form a sample data to be tested by each operating parameter and the corresponding change rate value;
第一预警模块，用于将所述待测样本数据输入到策树分类器中，并输出锅炉的潜在故障类型。The first early warning module is used to input the sample data to be tested into the policy tree classifier, and output the potential failure type of the boiler.
作为上述技术方案的进一步改进，识别装置还包括第二预警模块，所述第二预警模块替代第一预警模块，所述第二预警模块用于设置贝叶斯分类器，并将所述待测样本数据输入到贝叶斯分类器中，贝叶斯分类器输出锅炉的潜在故障类型的发生概率。As a further improvement of the above technical solution, the identification device also includes a second early warning module, which replaces the first early warning module, and the second early warning module is used to set a Bayesian classifier, and the The sample data is input into the Bayesian classifier, and the Bayesian classifier outputs the probability of occurrence of potential failure types of the boiler.
作为上述技术方案的进一步改进，所述传感器模块包括温度传感器、压力传感器、编码器、电压互感器以及电流互感器。As a further improvement of the above technical solution, the sensor module includes a temperature sensor, a pressure sensor, an encoder, a voltage transformer and a current transformer.
作为上述技术方案的进一步改进，所述第二计算模块中，包括初始化并训练神经网络模型，根据输入的控制参数，所述神经网络模型分别计算各个运行参数及其对应的变化率值的预警范围。As a further improvement of the above technical solution, the second calculation module includes initialization and training of the neural network model, and according to the input control parameters, the neural network model calculates the early warning range of each operating parameter and its corresponding change rate value .
本发明的有益效果是：本发明通过所采集的锅炉的实时运行参数，计算各个运行参数的变化率值，同时根据运行参数及其变化率值，首先判断其数据本身是否存在异常，最后利用决策分类器判断锅炉运行过程中的潜在故障，有效降低锅炉运行过程中的故障风险。The beneficial effects of the present invention are: the present invention calculates the rate of change of each operating parameter through the collected real-time operating parameters of the boiler; The classifier judges potential faults during boiler operation, effectively reducing the risk of failure during boiler operation.
附图说明Description of drawings
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单说明。显然，所描述的附图只是本发明的一部分实施例，而不是全部实施例，本领域的技术人员在不付出创造性劳动的前提下，还可以根据这些附图获得其他设计方案和附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly describe the drawings that need to be used in the description of the embodiments. Apparently, the described drawings are only some embodiments of the present invention, not all embodiments, and those skilled in the art can obtain other designs and drawings based on these drawings without creative work.
图1是本发明的方法流程示意图。Fig. 1 is a schematic flow chart of the method of the present invention.
具体实施方式Detailed ways
以下将结合实施例和附图对本发明的构思、具体结构及产生的技术效果进行清楚、完整的描述，以充分地理解本发明的目的、特征和效果。显然，所描述的实施例只是本申请的一部分实施例，而不是全部实施例，基于本申请的实施例，本领域的技术人员在不付出创造性劳动的前提下所获得的其他实施例，均属于本申请保护的范围。另外，文中所提到的所有连接关系，并非单指构件直接相接，而是指可根据具体实施情况，通过添加或减少连接辅件，来组成更优的连接结构。本发明创造中的各个技术特征，在不互相矛盾冲突的前提下可以交互组合。最后需要说明的是，如文中术语“中心、上、下、左、右、竖直、水平、内、外”等指示的方位或位置关系则为基于附图所示的方位或位置关系，仅是为了便于描述本技术方案和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本申请的限制。The concept, specific structure and technical effects of the present invention will be clearly and completely described below in conjunction with the embodiments and accompanying drawings, so as to fully understand the purpose, features and effects of the present invention. Apparently, the described embodiments are only some of the embodiments of the present application, not all of them. Based on the embodiments of the present application, other embodiments obtained by those skilled in the art without creative efforts belong to The protection scope of this application. In addition, all connection relationships mentioned in this article do not refer to the direct connection of components, but mean that a better connection structure can be formed by adding or reducing connection accessories according to specific implementation conditions. The various technical features in the invention can be combined interactively on the premise of not conflicting with each other. Finally, it should be noted that the orientations or positional relationships indicated by terms such as "center, upper, lower, left, right, vertical, horizontal, inner, outer" in the text are based on the orientations or positional relationships shown in the accompanying drawings, only It is for the convenience of describing the technical solution and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the application.
参照图1，本申请公开了一种锅炉潜在故障识别方法，主要应用于发电用锅炉系统场合，其第一实施例包括以下步骤：Referring to FIG. 1 , the present application discloses a boiler potential fault identification method, which is mainly applied to boiler systems for power generation. The first embodiment includes the following steps:
步骤100，初始化决策树分类器，输入多组训练样本数据，完成决策树分类器的训练操作；Step 100, initialize the decision tree classifier, input multiple sets of training sample data, and complete the training operation of the decision tree classifier;
步骤200，实时采集锅炉的多个运行参数；Step 200, collecting multiple operating parameters of the boiler in real time;
步骤300，分别计算各个运行参数的变化趋势，得到各个运行参数所对应的变化率值；Step 300, respectively calculate the change trend of each operating parameter, and obtain the change rate value corresponding to each operating parameter;
步骤400，根据输入的控制参数，分别计算各个运行参数及其对应的变化率值的预警范围；Step 400, according to the input control parameters, respectively calculate the early warning ranges of each operating parameter and its corresponding change rate value;
步骤500，分别判断各个运行参数是否在预警范围内，如果是，输出预警信号；Step 500, respectively judge whether each operating parameter is within the warning range, if yes, output a warning signal;
步骤600，将各个运行参数以及所对应的变化率值组成一个待测样本数据；Step 600, each operating parameter and the corresponding change rate value form a sample data to be tested;
步骤700，将所述待测样本数据输入到策树分类器中，所述决策树分类器输出锅炉的潜在故障类型。Step 700, input the sample data to be tested into a decision tree classifier, and the decision tree classifier outputs the potential failure type of the boiler.
具体地，本实施例中通过所采集的锅炉的实时运行参数，计算各个运行参数的变化率值，同时根据运行参数及其变化率值，首先判断其数据本身是否存在异常，最后利用决策分类器判断锅炉运行过程中的潜在故障，有效降低锅炉运行过程中的故障风险。Specifically, in this embodiment, the collected real-time operating parameters of the boiler are used to calculate the rate of change of each operating parameter, and at the same time, according to the operating parameters and their rate of change, first determine whether the data itself is abnormal, and finally use the decision classifier Identify potential failures during boiler operation and effectively reduce the risk of failure during boiler operation.
进一步作为优选的实施方式，本实施例中，所述运行参数包括炉内温度、炉内气压、燃烧室温度、燃烧室气压、送风机转速、发电机转速以及输出电参数。具体地，本实施例主要是通过炉内温度、炉内气压、燃烧室温度、燃烧室气压、送风机转速、发电机转速以及输出电参数，及各个参数所对应的变化率值对锅炉的故障或者潜在故障进行识别。As a further preferred embodiment, in this embodiment, the operating parameters include furnace temperature, furnace air pressure, combustion chamber temperature, combustion chamber air pressure, blower speed, generator speed, and output electrical parameters. Specifically, this embodiment is mainly based on the temperature in the furnace, the air pressure in the furnace, the temperature in the combustion chamber, the air pressure in the combustion chamber, the rotation speed of the blower, the rotation speed of the generator, and the output electrical parameters, and the change rate values corresponding to each parameter. Identify potential faults.
进一步作为优选的实施方式，本实施例中，步骤400中包括初始化并训练神经网络模型，根据输入的控制参数，所述神经网络模型分别计算各个运行参数及其对应的变化率值的预警范围。具体地，本实施例是利用神经网络模型计算各个运行参数及其对应的变化率值的预警范围，基于大量的数据对锅炉正常运行过程中的运行参数及其变化率值进行统计，能够有效地提高各个运行参数及其对应的变化率值的预警范围的计算准确度，保证锅炉运行时的安全性。As a further preferred embodiment, in this embodiment, step 400 includes initializing and training the neural network model, and according to the input control parameters, the neural network model calculates the early warning ranges of each operating parameter and its corresponding change rate value. Specifically, in this embodiment, the neural network model is used to calculate the early warning range of each operating parameter and its corresponding rate of change, and based on a large amount of data, the operating parameters and their rate of change in the normal operation of the boiler are counted, which can effectively Improve the calculation accuracy of the early warning range of each operating parameter and its corresponding change rate value, and ensure the safety of the boiler during operation.
本申请所述识别方法的第二实施例，与第一实施例相比区别在于将步骤700替代为步骤800，设置贝叶斯分类器，将所述待测样本数据输入到贝叶斯分类器中，所述贝叶斯分类器输出锅炉的潜在故障类型的发生概率。识别方法的第二实施例中，利用贝叶斯分类器能够准确地判断出锅炉的潜在故障类型的发生概率，这是决策树分类器所不具备的功能，本实施例能够提供潜在故障的发生概率，相关工作人员能够根据潜在故障的发生概率判断是否需要采取相应的应急措施。The second embodiment of the identification method described in the present application differs from the first embodiment in that step 700 is replaced by step 800, a Bayesian classifier is set, and the sample data to be tested is input to the Bayesian classifier , the Bayesian classifier outputs the probability of occurrence of potential failure types of the boiler. In the second embodiment of the identification method, the Bayesian classifier can be used to accurately determine the probability of occurrence of the potential failure type of the boiler, which is a function that the decision tree classifier does not have. This embodiment can provide the occurrence probability of potential failure types. Probability, the relevant staff can judge whether it is necessary to take corresponding emergency measures according to the probability of potential failure.
本申请同时还公开了一种锅炉潜在故障识别装置，其第一实施例，包括：The present application also discloses a boiler potential fault identification device, the first embodiment of which includes:
决策树分类器生成模块，用于初始化决策树分类器，并且完成决策树分类器的训练操作；Decision tree classifier generation module, used to initialize the decision tree classifier, and complete the training operation of the decision tree classifier;
传感器模块，用于实时采集锅炉的多个运行参数；The sensor module is used to collect multiple operating parameters of the boiler in real time;
第一计算模块，用于分别计算各个运行参数的变化趋势，得到各个运行参数所对应的变化率值；The first calculation module is used to separately calculate the change trend of each operating parameter, and obtain the change rate value corresponding to each operating parameter;
第二计算模块，用于根据输入的控制参数，分别计算各个运行参数及其对应的变化率值的预警范围；The second calculation module is used to calculate the early warning ranges of each operating parameter and its corresponding change rate value according to the input control parameters;
判断模块，用于分别判断各个运行参数是否在预警范围内，如果是，输出预警信号；A judging module, used to judge whether each operating parameter is within the early warning range, and if so, output an early warning signal;
样本生成模块，用于将各个运行参数以及所对应的变化率值组成一个待测样本数据；The sample generation module is used to form a sample data to be tested by each operating parameter and the corresponding change rate value;
第一预警模块，用于将所述待测样本数据输入到策树分类器中，并输出锅炉的潜在故障类型。The first early warning module is used to input the sample data to be tested into the policy tree classifier, and output the potential failure type of the boiler.
进一步作为优选的实施方式，本实施例中，所述传感器模块包括温度传感器、压力传感器、编码器、电压互感器以及电流互感器。其中所述温度传感器用于检测炉内温度以及燃烧室温度，所述压力传感器用于检测炉内气压以及燃烧室气压，所述编码器用于检测送风机转速以及发电机转速，所述电压互感器以及电流互感器用于检测发电机输出电参数。As a further preferred implementation manner, in this embodiment, the sensor module includes a temperature sensor, a pressure sensor, an encoder, a voltage transformer and a current transformer. The temperature sensor is used to detect the temperature in the furnace and the temperature of the combustion chamber, the pressure sensor is used to detect the air pressure in the furnace and the air pressure in the combustion chamber, the encoder is used to detect the speed of the blower and the speed of the generator, the voltage transformer and Current transformers are used to detect generator output electrical parameters.
进一步作为优选的实施方式，本实施例中，所述第二计算模块中，包括初始化并训练神经网络模型，根据输入的控制参数，所述神经网络模型分别计算各个运行参数及其对应的变化率值的预警范围。As a further preferred implementation, in this embodiment, the second calculation module includes initialization and training of the neural network model, and according to the input control parameters, the neural network model calculates each operating parameter and its corresponding rate of change The warning range of the value.
本申请所述锅炉潜在故障识别装置的第二实施例，与第一实施例相比，其区别在于所述识别装置还包括第二预警模块，所述第二预警模块替代第一预警模块，所述第二预警模块用于设置贝叶斯分类器，并将所述待测样本数据输入到贝叶斯分类器中，贝叶斯分类器输出锅炉的潜在故障类型的发生概率。Compared with the first embodiment, the second embodiment of the boiler potential fault identification device described in this application differs in that the identification device also includes a second early warning module, which replaces the first early warning module, so The second early warning module is used to set a Bayesian classifier, and input the sample data to be tested into the Bayesian classifier, and the Bayesian classifier outputs the occurrence probability of potential failure types of the boiler.
以上对本申请的较佳实施方式进行了具体说明，但本申请并不限于所述实施例，熟悉本领域的技术人员在不违背本发明精神的前提下还可作出种种的等同变型或替换，这些等同的变型或替换均包含在本申请权利要求所限定的范围内。The preferred embodiments of the present application have been specifically described above, but the present application is not limited to the described embodiments, and those skilled in the art can also make various equivalent modifications or replacements without departing from the spirit of the present invention. Equivalent modifications or replacements are all included within the scope defined by the claims of the present application.
Claims (8)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910547774.9A CN110345463A (en) | 2019-06-24 | 2019-06-24 | A kind of boiler incipient fault recognition methods and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910547774.9A CN110345463A (en) | 2019-06-24 | 2019-06-24 | A kind of boiler incipient fault recognition methods and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110345463A true CN110345463A (en) | 2019-10-18 |
Family
ID=68182835
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910547774.9A Pending CN110345463A (en) | 2019-06-24 | 2019-06-24 | A kind of boiler incipient fault recognition methods and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110345463A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110579675A (en) * | 2019-10-30 | 2019-12-17 | 国投云顶湄洲湾电力有限公司 | Load short circuit identification method, device, equipment and storage medium |
| CN111027620A (en) * | 2019-12-09 | 2020-04-17 | 青岛特利尔环保集团股份有限公司 | Boiler operation parameter processing method and device, boiler controller and medium |
| CN111998690A (en) * | 2020-08-24 | 2020-11-27 | 中国测试技术研究院 | Fault alarm method for smoke exhaust fan of low-temperature kiln |
| CN112215503A (en) * | 2020-10-19 | 2021-01-12 | 青岛鹏海软件有限公司 | Reliability monitoring method based on SPC |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102331772A (en) * | 2011-03-30 | 2012-01-25 | 浙江省电力试验研究院 | A method for early warning and fault diagnosis of abnormal superheated steam temperature of DC million units |
| CN104037103A (en) * | 2013-03-04 | 2014-09-10 | 阿自倍尔株式会社 | Fault Detecting System And Fault Detecting Method |
| CN105974793A (en) * | 2016-05-04 | 2016-09-28 | 华中科技大学 | Power plant boiler combustion intelligent control method |
| CN108133326A (en) * | 2017-12-22 | 2018-06-08 | 华润电力（菏泽）有限公司 | A kind of status early warning method and system based on thermal power generating equipment |
| CN108932531A (en) * | 2018-07-09 | 2018-12-04 | 朱卫列 | Equipment state on-line checking algorithm based on model feature value |
| CN109635873A (en) * | 2018-12-19 | 2019-04-16 | 佛山科学技术学院 | A kind of UPS failure prediction method |
-
2019
- 2019-06-24 CN CN201910547774.9A patent/CN110345463A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102331772A (en) * | 2011-03-30 | 2012-01-25 | 浙江省电力试验研究院 | A method for early warning and fault diagnosis of abnormal superheated steam temperature of DC million units |
| CN104037103A (en) * | 2013-03-04 | 2014-09-10 | 阿自倍尔株式会社 | Fault Detecting System And Fault Detecting Method |
| CN105974793A (en) * | 2016-05-04 | 2016-09-28 | 华中科技大学 | Power plant boiler combustion intelligent control method |
| CN108133326A (en) * | 2017-12-22 | 2018-06-08 | 华润电力（菏泽）有限公司 | A kind of status early warning method and system based on thermal power generating equipment |
| CN108932531A (en) * | 2018-07-09 | 2018-12-04 | 朱卫列 | Equipment state on-line checking algorithm based on model feature value |
| CN109635873A (en) * | 2018-12-19 | 2019-04-16 | 佛山科学技术学院 | A kind of UPS failure prediction method |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110579675A (en) * | 2019-10-30 | 2019-12-17 | 国投云顶湄洲湾电力有限公司 | Load short circuit identification method, device, equipment and storage medium |
| CN110579675B (en) * | 2019-10-30 | 2022-03-01 | 国投云顶湄洲湾电力有限公司 | Load short circuit identification method, device, equipment and storage medium |
| CN111027620A (en) * | 2019-12-09 | 2020-04-17 | 青岛特利尔环保集团股份有限公司 | Boiler operation parameter processing method and device, boiler controller and medium |
| CN111998690A (en) * | 2020-08-24 | 2020-11-27 | 中国测试技术研究院 | Fault alarm method for smoke exhaust fan of low-temperature kiln |
| CN111998690B (en) * | 2020-08-24 | 2021-05-25 | 中国测试技术研究院 | Fault alarm method for smoke exhaust fan of low-temperature kiln |
| CN112215503A (en) * | 2020-10-19 | 2021-01-12 | 青岛鹏海软件有限公司 | Reliability monitoring method based on SPC |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110345463A (en) | A kind of boiler incipient fault recognition methods and device | |
| CN109915736B (en) | Thermal pipe network system and leakage detection method thereof | |
| CN104767482B (en) | A kind of photovoltaic module is aging and short trouble inline diagnosis method | |
| CN110045594B (en) | Intelligent management and control system and method for predicting state risk of four tubes of boiler | |
| CN106642059A (en) | Safety online monitoring method for high-temperature heated faces of utility boiler | |
| CN103016948A (en) | Online leak detection device for steam trap of steaming-water pipeline of thermal power plant | |
| CN111651933B (en) | Industrial boiler fault early warning method and system based on statistical inference | |
| CN106226621A (en) | A kind of secondary device fault diagnosis based on grey correlation analysis and method for early warning | |
| CN101907685A (en) | Correctness inspection method of generator set operating parameters adapted to changes in operating conditions | |
| CN103471729A (en) | Device temperature early warning method and application thereof | |
| CN110068047A (en) | A kind of household base heating water heater operation cloud monitoring method and its system | |
| CN110472274A (en) | A kind of boiler flow field thermal deviation model real-time predicting method | |
| CN117674420A (en) | Power station operating status monitoring system and method based on multivariate time series prediction | |
| CN103925155A (en) | Self-adaptive detection method for abnormal wind turbine output power | |
| CN117037454A (en) | Early warning protection system, control method and device of electrical cabinet, medium and electrical cabinet | |
| CN111898794B (en) | An abnormal monitoring method for thermal efficiency of large coal-fired boilers | |
| CN107895194A (en) | A kind of nuclear power plant's main coolant system method for diagnosing faults | |
| CN202582601U (en) | Cogeneration boiler heating surface state detection, analysis and management system | |
| CN103411204B (en) | The method of the low alarm condition trigger fault diagnosis of oxygen-eliminating device pressure | |
| CN110454766A (en) | A boiler early warning method and early warning device | |
| CN112087922A (en) | Power equipment inspection and automatic alarm system | |
| CN203101373U (en) | Automatic alarm device for monitoring amount of dissolved oxygen in condensed water | |
| CN109974311B (en) | Solar energy leakage detection system | |
| CN106382842A (en) | Pipe explosion monitoring method and system for cement plant waste heat recovery multi-loop heat exchanger | |
| CN109325294B (en) | A Construction Method of Evidence Characterization for the Performance State of Air Preheater of Thermal Power Units |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191018 |