Fault quick checking method and system for fire-fighting equipment maintenance
By calculating the failure probability using machine learning models and Naive Bayes classifiers, and combining this with intelligent system integration, the problem of low efficiency in fault identification and troubleshooting during fire equipment maintenance is solved, achieving efficient and accurate fault diagnosis and optimized maintenance paths.
Patent Information
- Application Number
- CN202511124970.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-12
- Publication Date
- 2025-11-07
AI Technical Summary
In the maintenance of existing fire-fighting equipment, the efficiency of fault identification and troubleshooting is low, the misjudgment rate is high, and there is a lack of systematic and data-driven auxiliary tools, resulting in long maintenance cycles and an inability to dynamically adjust and effectively utilize historical information.
Machine learning models are used to encode and analyze the features of fault phenomena. A Naive Bayes classifier is used to calculate the fault probability and generate priority troubleshooting suggestions. Dynamic diagnostic support is provided by integrating a database, data preprocessing, learning platform and feedback module through an intelligent system.
It significantly improves the efficiency and accuracy of fault diagnosis, reduces the subjectivity and misjudgment rate of manual judgment, enhances the pertinence and efficiency of maintenance, and promotes the transformation of maintenance methods from experience-driven to data-driven.
Smart Images
Figure CN120910702A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of equipment maintenance, and in particular to a fault rapid search method and system for fire-fighting equipment maintenance. BACKGROUND
[0002] In the field of fire-fighting equipment maintenance, such as daily operation and maintenance of B-type foam systems, motorized chain saws and other equipment, rapid identification and troubleshooting of faults is a key link to ensure reliable operation of equipment and improve emergency response efficiency. However, current grassroots equipment technicians generally face problems such as weak maintenance foundation and insufficient fault diagnosis capability, which leads to reliance on experience judgment when facing complex system faults, lack of systematic and data-driven auxiliary tools, and thus causes problems such as low troubleshooting efficiency, high misjudgment rate, and long maintenance period.
[0003] Traditional fault troubleshooting methods mainly rely on paper maintenance manuals or experiential rapid search tables. For example, by compiling an "equipment maintenance rapid search table", common fault phenomena and possible fault points are corresponded one by one to guide technicians to check one by one. However, this method has the following obvious limitations: The information in the rapid search table is fixed and cannot be dynamically adjusted according to differences in equipment model, operating environment, and use intensity, resulting in a lack of targeted troubleshooting path; The same fault phenomenon may correspond to multiple potential fault points, but the existing method does not provide a sorting mechanism for the fault probability of components, resulting in the need for technicians to check one by one, which is time-consuming and labor-intensive; With the accumulation of maintenance data, a large amount of valuable historical information is not effectively mined and utilized, and intelligent support for fault prediction and troubleshooting path cannot be formed.
[0004] Based on this, the present application is proposed. SUMMARY
[0005] One of the purposes of the present application is to provide a fault rapid search method for fire-fighting equipment maintenance, to improve the efficiency and accuracy of fault diagnosis, and to meet the development needs of intelligent operation and maintenance of fire-fighting equipment.
[0006] In order to achieve the above purpose, the technical solution of the present application is as follows: A fault rapid search method for fire-fighting equipment maintenance, comprising the following steps: S10. Inputting a fault phenomenon; S20. Feature coding of the input fault phenomenon, converting it into a numerical feature suitable for machine learning model processing; S30. The machine learning model analyzes the input numerical feature, gives a fault troubleshooting suggestion, and gives a solution according to the suggestion.
[0007] Further, in the step S10, the field of the input fault phenomenon is the description field of the fault in the maintenance quick reference table, and the maintenance quick reference table is formed by technicians; In the step S20, the feature coding includes the following process: according to the coding table, the field of the input fault phenomenon is mapped to a unique integer value, and the conversion from the text type fault description to the numerical type feature is completed.
[0008] Further, the machine learning model is trained by using historical maintenance data, can classify and predict the input fault phenomenon, and outputs fault component information and corresponding solutions with a fault probability greater than a set threshold.
[0009] Further, in the output fault component information with a fault probability greater than a set threshold, the probability of each component failure is included, and the probability value is arranged from large to small according to the probability value, and a priority troubleshooting suggestion is generated; The probability of each component failure is calculated by analyzing historical maintenance data using a Naive Bayes classifier.
[0010] Further, in the calculation of the probability of each component failure, one or more of the working load data of the component, the working environment temperature and humidity data of the component, and the inspection and maintenance data of the component are introduced, and the result calculated by using the Naive Bayes classifier is optimized.
[0011] The second object of the present application is to provide a system based on the above-mentioned fault quick search method for fire fighting equipment maintenance, comprising: A fault database for storing various predefined fault types and corresponding solutions; A data preprocessing module responsible for feature coding of the input fault phenomenon and converting it into numerical features suitable for model processing; A machine learning module for analyzing the input numerical features, giving fault troubleshooting suggestions, and giving solutions for the suggestions, while calculating the probability of each component failure, arranging the components that may fail in probability value from large to small according to the calculation result, and generating a priority-ordered troubleshooting guide.
[0012] Further, a learning platform is included, which synchronously outputs the maintenance teaching video link of the components that may fail according to the fault troubleshooting suggestion.
[0013] Further, a technical support module is included, which synchronously outputs the contact information of the factory technicians of the components that may fail according to the fault troubleshooting suggestion.
[0014] Further, a model updating module is included for periodically extracting data from newly collected maintenance records to retrain the machine learning model.
[0015] Further, a user feedback module is included for receiving ratings and feedback information of the troubleshooting suggestions and solutions output by the system from maintenance personnel, and using the feedback data for model training and optimization.
[0016] The advantages of the present application are that the machine learning model automatically analyzes the input failure phenomenon, quickly outputs possible failure points, significantly reduces the subjectivity and misjudgment rate of manual judgment, uses a probability calculation model (Naive Bayes classifier) to calculate the probability of component failure and sorts them from high to low according to the failure probability, provides priority troubleshooting suggestions for maintenance personnel, avoids the inefficient way of "blind search" and "one-by-one search", and further introduces dynamic factors such as work load, environmental temperature and humidity, and maintenance, combines historical maintenance data to make the failure probability prediction more close to the actual operation state of the equipment, improve the diagnosis accuracy, and effectively solve the problems of unclear troubleshooting path, low efficiency and low data utilization rate in traditional maintenance methods. BRIEF DESCRIPTION OF DRAWINGS
[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of the provided drawings.
[0018] Figure 1 A flowchart of a fault rapid search method for fire-fighting equipment maintenance is provided in a specific embodiment of the present application. DETAILED DESCRIPTION
[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0020] The present embodiment proposes a fault rapid search method for fire-fighting equipment maintenance, comprising the following steps.
[0021] S10. The operator inputs the fault phenomenon, and the field of the input fault phenomenon is the description field of the maintenance quick reference table, which is compiled by professional technicians according to typical fault types of fire-fighting equipment, and covers common fault phenomena of multiple key functional modules of various fire-fighting equipment. As shown in the following table, it is a fault quick reference table of a B-type foam system, and the fault quick reference tables of the remaining equipment are similar and are not shown.
[0022] Table 1: Fault quick reference table of B-type foam system
[0023]
[0024]
[0025] Step S20. The input fault phenomenon is characterized and coded, which is converted into a numerical feature suitable for processing by a machine learning model. The feature coding includes the following process: according to the coding table, the field of the input fault phenomenon is mapped to a unique integer value, and the conversion from the text type fault description to the numerical type feature is completed. For example, the fault phenomena in Table 1 include "foam out too thick", "vacuum pumping without pressure retention", and other different fault types. Through label coding, "foam out too thick" may be coded as 1, "vacuum pumping without pressure retention" is coded as 2, and so on. When the operator inputs the fault phenomenon "foam out too thick", the system encodes this field as 1 and inputs it into the machine learning model.
[0026] Step S30. The machine learning model analyzes the input numerical feature, gives fault troubleshooting suggestions, and gives solutions according to the suggestions.
[0027] In this embodiment, the machine learning model is trained using historical maintenance data, which can classify and predict the input fault phenomenon, output the information of the parts that may fail, and the corresponding solutions. The historical maintenance data includes but is not limited to: fault occurrence time, equipment model, fault phenomenon description, actual fault parts, maintenance personnel feedback, maintenance results, etc.
[0028] The same fault phenomenon may correspond to multiple potential fault points. The existing method generally involves technicians checking each part that may be involved, which is time-consuming and laborious. In the event of an emergency, the fire-fighting equipment may not be repaired in time, resulting in huge losses. As an improvement, the embodiment includes the probability of failure of each part in the output of the parts that may fail, and arranges the parts that may fail in descending order of probability value to generate a priority troubleshooting suggestion, avoiding the inefficient way of "blind search" and "one-by-one search", and effectively solving the problems of unclear troubleshooting path, low efficiency, and low data utilization in traditional maintenance quick reference methods.
[0029] In this embodiment, the probability of failure of each component is calculated by analyzing historical maintenance data using a Naive Bayes classifier; the calculation result is as follows: 。
[0030] In the formula: represents the component failure probability value calculated by analyzing historical maintenance data using a Naive Bayes classifier.
[0031] is the conditional probability, which is calculated by counting the number of times the current failure phenomenon occurs when the component fails, and the specific formula is as follows: ; Correspondingly, the conditional probability of the component not failing can be obtained ; is the prior probability, which is calculated by counting the frequency of failure of each component in all maintenance records, and the specific formula is as follows:
[0032] Correspondingly, the prior probability of the component not failing can be obtained ; is the marginal probability, which is calculated by the total probability formula, and the specific formula is as follows: .
[0033] The probability of failure of each component is calculated only by historical maintenance data, but the actual failure rate is related to historical maintenance data, recent workload, environmental temperature and humidity, and inspection and maintenance (recently maintained, the failure probability is relatively low). To make the failure probability prediction more close to the actual operation state of the equipment and improve the diagnostic accuracy, this embodiment introduces the working load data of the component, the working environment temperature and humidity data of the component, and the inspection and maintenance data of the component to optimize the result calculated by the Naive Bayes classifier.
[0034] The working load data of the component affects The working load factor Wi is introduced, the working environment temperature and humidity data of the component affect The environmental temperature and humidity factor Ei is introduced, and the inspection and maintenance data of the component affect The maintenance factor Mi is introduced.
[0035] The work load factor is a linear function of the part working time and the average load rate, and the specific formula is as follows: ; In the formula, a, b, and c are coefficients obtained by fitting analysis of historical data.
[0036] The environmental temperature and humidity factor is an exponential function considering temperature and humidity, and the specific formula is as follows: ; In the formula, and are the optimal operating temperature and humidity, respectively, and D and E are adjustment parameters.
[0037] The maintenance factor is a decay function considering the part state score and maintenance time, and the specific formula is as follows: ; Where k is the time decay coefficient; the maintenance score is filled in by the maintenance personnel after completing the maintenance operation, and the score range is 0-10 points, with 10 points being the highest maintenance quality; the last maintenance time represents the number of days from the completion of the last maintenance to the occurrence of the current fault.
[0038] The formula for calculating the comprehensive probability value of the part failure is as follows: .
[0039] Based on the above method, the embodiment also proposes a fault rapid search system for fire fighting equipment maintenance, comprising: A fault database for storing various predefined fault types and their corresponding solutions; A data preprocessing module responsible for feature encoding of the input fault phenomenon and converting it into numerical features suitable for model processing; A machine learning module that analyzes the input numerical features, provides fault troubleshooting suggestions, and provides solutions for the suggestions, while calculating the probability of failure of each part, arranging the parts that may fail in probability value from large to small according to the calculation results, and generating a troubleshooting guide with priority ranking; A learning platform that synchronously outputs the maintenance teaching video link of the parts that may fail according to the fault troubleshooting suggestions, to assist maintenance personnel in quickly mastering the maintenance points; A technical support module that synchronously outputs the contact information of the factory technical personnel of the parts that may fail according to the fault troubleshooting suggestions, to facilitate maintenance personnel to contact professional technical personnel for further guidance in a timely manner; A model updating module for periodically extracting data from newly collected maintenance records to retrain the machine learning model, to continuously optimize the prediction accuracy of the model; A user feedback module is configured to receive scores and feedback information of the troubleshooting suggestions and solutions output by the system from the maintenance personnel, and use the feedback data for model training and optimization.
[0040] The embodiment introduces an intelligent algorithm model, a multi-factor fusion probability calculation mechanism, user feedback closed-loop optimization and a multi-module integrated system design, which not only significantly improves the efficiency and accuracy of troubleshooting of the fire-fighting equipment, but also promotes the change of the maintenance mode from experience driving to data driving and intelligent assistance, and has important technical value and application prospect.
[0041] The above embodiments are only used for explaining the concept of the present application, and are not a limitation on the protection of the present application. Any non-essential modification of the present application shall fall within the scope of protection of the present application.
Claims
1. A method for troubleshooting of fire fighting equipment maintenance, characterized in that, Comprising the following steps: S10. Input the fault phenomenon; S20. Characteristic coding is performed on the input fault phenomenon, which is converted into numerical features suitable for machine learning model processing; S30. The machine learning model analyzes the input numerical features, gives troubleshooting suggestions, and gives solutions according to the suggestions.
2. A method for troubleshooting of fire fighting equipment maintenance as claimed in claim 1 wherein, In step S10, the field of input fault phenomenon is the description field of fault in the maintenance quick reference table, which is formed by technicians; In step S20, the characteristic coding Comprising the following process: According to the coding table, the field of input fault phenomenon is mapped to a unique integer value, completing the conversion from text type fault description to numerical features.
3. A method for troubleshooting of fire fighting equipment maintenance as claimed in claim 1 wherein, The machine learning model is trained using historical maintenance data and can classify and predict the input fault phenomenon, output fault component information with fault probability greater than a set threshold and corresponding solutions.
4. A method for troubleshooting of fire fighting equipment maintenance as claimed in claim 3 wherein, In the output fault component information with fault probability greater than a set threshold, the probability of each component failure is included, and the probability values are arranged from large to small according to the probability value size to generate priority troubleshooting suggestions; The probability of each component failure is calculated by analyzing historical maintenance data using a Naive Bayes classifier.
5. A method for troubleshooting of fire fighting equipment maintenance as claimed in claim 4 wherein, When calculating the probability of each component failure, one or more of the workload data of the component, the temperature and humidity data of the working environment of the component, and the inspection and maintenance data of the component are introduced to optimize the results calculated by the Naive Bayes classifier.
6. A system for failure quick search method for maintenance of fire fighting equipment based on any one of claims 1 to 5, characterized in that, Comprising: Fault database, used to store various predefined fault types and their corresponding solutions; Data preprocessing module, responsible for feature coding of input fault phenomenon, which is converted into numerical features suitable for model processing; Machine learning module, which analyzes the input numerical features, gives troubleshooting suggestions, and gives solutions according to the suggestions, while calculating the probability of each component failure, arranging the components that may fail in probability value from large to small according to the calculation results, and generating priority troubleshooting guide.
7. A fault locating system for maintenance of fire fighting equipment as claimed in claim 6 wherein, Including learning platform, according to the fault troubleshooting suggestion, the maintenance teaching video link of the component that may fail is output synchronously.
8. A fault locating system for maintenance of fire fighting equipment as claimed in claim 6 wherein, Including technical support module, according to the fault troubleshooting suggestion, the contact information of the factory technical personnel of the component that may fail is output synchronously.
9. A fault locating system for maintenance of fire fighting equipment as claimed in claim 6 wherein, Including model updating module, used to extract data from newly collected maintenance records to retrain the machine learning model.
10. A fault locating system for maintenance of fire fighting equipment as claimed in claim 6 wherein, Including user feedback module, used to receive maintenance personnel's score and feedback information on the system output troubleshooting suggestions and solutions, and use the feedback data for model training and optimization.