A fault analysis method for an air conditioning system of a motor train unit

Abstract

The application discloses a kind of motor train unit air conditioning system fault analysis methods, this method includes: the multiple-source fault data of collecting ground inspection fault data and operation fault data;The association between fault component and fault source is established by pre-processing fault data;Statistical analysis of the fault proportion of each component in different fault sources, identify fault overlapping components;Analysis of the association law of fault overlapping components and seasonal environmental changes, extract the high-incidence fault component characteristics of different seasons;Failure mode analysis is carried out on key components such as controller, temperature sensor, auxiliary contact of contactor, through station electric heater;Differentiated preventive measures are developed for different seasons and different components, and a fault analysis method system is formed. Realize the system integration of multiple-source fault data, reveal the seasonal failure law, accurately locate the fault root cause, and form a systematic differentiated preventive measures system, which can effectively reduce the overall failure rate of motor train unit air conditioning system.

Description

Technical Field

[0001] This invention relates to the field of railway transportation, and in particular to a method for fault analysis of air conditioning systems in high-speed trains. Background Technology

[0002] With the continuous development of rail transit equipment technology, high-speed trains, as an important carrier of modern public transportation, are receiving increasing attention for their operational quality and passenger experience. The air conditioning system, as one of the key auxiliary systems of high-speed trains, undertakes the core functions of regulating the temperature inside the train, purifying the air, and ensuring passenger comfort. Its operating status directly affects the overall operational efficiency and service quality of the high-speed train. The air conditioning system of a high-speed train has a complex structure, involving switching between multiple operating modes such as cooling, heating, and ventilation. It includes multiple core components such as compressors, condensers, evaporators, controllers, temperature sensors, and electric heaters. These components work together to form a complete closed-loop temperature regulation system.

[0003] Among them, fault analysis of the air conditioning system of high-speed trains is a crucial technical aspect to ensure the safe operation of trains. Air conditioning system faults can be categorized into two dimensions based on their source: ground inspection faults and operational faults. Ground inspection faults mainly include automatic early warning faults from the high-speed train's remote monitoring system and faults discovered during manual inspections upon entering the depot for maintenance. Operational faults encompass HMI (Human-Machine Interface) pop-up alarm faults during train operation and sudden faults discovered during onboard mechanics' inspections. Due to significant differences in the working principles and operating conditions of various components within the air conditioning system, the fault types, mechanisms, and frequencies of different components exhibit obvious heterogeneity.

[0004] However, existing fault analysis methods lack the ability to systematically integrate fault data from multiple sources. This makes it difficult to establish an effective correlation between potential hazards discovered during ground inspections and actual faults occurring during operation, hindering the accurate identification of critical fault components with high overlap rates. Furthermore, traditional analysis methods typically employ single-dimensional fault statistics, failing to reveal the inherent correlation between fault occurrence and seasonal environmental changes, and thus unable to develop differentiated preventative measures for high-incidence fault types in different seasons. In addition, existing technologies lack in-depth analysis of the failure mechanisms of different components in air conditioning systems, failing to fully consider the failure modes of critical components such as controllers, sensors, contactors, and electric heaters under specific operating conditions. This makes root cause location difficult, hindering the fundamental reduction of the overall failure rate of air conditioning systems. Summary of the Invention

[0005] The purpose of this invention is to provide a fault analysis method for the air conditioning system of high-speed trains, which solves the problems mentioned in the background art.

[0006] This invention is implemented as follows: a fault analysis method for an air conditioning system of a high-speed train, comprising the following steps: Step 1: Collect multi-source fault data of the EMU air conditioning system. This multi-source fault data includes ground inspection fault data and operational fault data. Ground inspection fault data includes automatic early warning faults from the EMU remote monitoring system and faults discovered during manual inspections upon entering the depot for maintenance. Operational fault data includes HMI pop-up alarm faults and sudden faults discovered during onboard mechanic inspections. Step 2: Preprocess the collected multi-source fault data to establish the correlation between faulty components and fault sources. Step 3: Based on the preprocessed fault data, statistically analyze the fault percentage of each component in different fault sources, identifying overlapping fault components in ground inspection and operational faults. Step 4: Based on the statistical analysis results, analyze the correlation between overlapping fault components and seasonal environmental changes, extracting the characteristics of high-incidence fault components in different seasons. Step 5: Perform failure mode analysis on each overlapping fault component, deeply analyzing the failure mechanisms of key components such as controllers, temperature sensors, contactor auxiliary contacts, and electric heaters under specific operating conditions. Step 6: Based on the failure mode analysis results, formulate differentiated preventative measures for different seasons and different components, forming a fault analysis methodology system for the EMU air conditioning system.

[0007] Preferably, the type of EMU for collecting fault data in step 1 is a certain type of EMU, the collection period is the whole year of operation data, and the collection scope covers all fault records of the EMU's air conditioning system under multiple working modes such as cooling, heating, and ventilation.

[0008] Preferably, the preprocessing operation in step 2 includes data cleaning, outlier removal, and fault classification standardization processing, establishing a fault component coding system, and uniformly mapping fault data from different sources to a standardized fault component classification table.

[0009] Preferably, the statistical analysis in step 3 uses a multidimensional fault statistics algorithm to calculate the proportion of each component in ground inspection faults and operational faults, as well as the overall proportion, and selects the fault-overlapping components with an overall proportion exceeding a preset threshold as the key analysis objects.

[0010] Preferably, in step 4, the seasonal correlation analysis divides the whole year into three periods: a winter peak period, a summer peak period, and a stable period. The failure frequency of different components in each period is statistically analyzed, and the characteristic failure components of each peak period are extracted. The winter peak period includes January and December, and the summer peak period includes June to August.

[0011] Preferably, the failure mode analysis of the air conditioner controller in step 5 includes relay configuration optimization analysis, the failure mode analysis of the temperature sensor includes potting process improvement and adhesive selection optimization analysis, the failure mode analysis of the contactor auxiliary contacts includes structural design improvement analysis, and the failure mode analysis of the Taidian heater includes fault alarm logic improvement and three-phase resistance measurement early troubleshooting analysis.

[0012] Preferably, the differentiated preventive measures in step 6 include: optimizing the relay configuration in the air conditioner controller for the high incidence period in winter, increasing redundant relays and improving their selection; improving the potting process to enhance sealing performance and selecting adhesive materials with a wider temperature range to address temperature sensor failure; improving the contactor auxiliary contact design to increase contact pressure and wear resistance to address contactor auxiliary contact failure; and improving the fault alarm logic to identify early anomalies and perform early fault diagnosis by measuring the three-phase resistance.

[0013] Preferably, the method further includes establishing a fault data continuous tracking mechanism, regularly updating statistical analysis results, dynamically adjusting the implementation parameters of differentiated preventive measures, and forming a closed-loop optimized fault analysis method.

[0014] Compared with existing technologies, this invention has the following advantages: By collecting and integrating ground inspection fault data and operational fault data, it achieves systematic integration of fault data from multiple sources, establishes an identification mechanism for overlapping fault components, and solves the problem of single data sources in traditional analysis methods; by statistically analyzing the correlation between overlapping fault components and seasonal environmental changes, it reveals the inherent correlation of high-incidence fault types in different seasons, providing data support for formulating differentiated preventive measures; by conducting failure mode analysis on key components such as controllers, temperature sensors, contactor auxiliary contacts, and electric heaters, it deeply analyzes the failure mechanisms of each component under specific operating conditions, achieving precise location of the root cause of the fault; by formulating differentiated preventive measures for different seasons and different components, it forms a systematic fault analysis methodology, which can effectively reduce the overall failure rate of the EMU air conditioning system, improve passenger comfort, and enhance the operational order of the EMU. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the entire process architecture of the fault analysis method for the air conditioning system of high-speed trains proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of multi-source fault data integration and seasonal correlation analysis in this invention. Detailed Implementation

[0016] Example 1 To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0017] The fault analysis method for high-speed train air conditioning systems provided by this invention includes the following core steps: collecting fault data from multiple sources, preprocessing to establish correlations, statistically analyzing to identify overlapping fault components, analyzing seasonal correlation patterns, performing failure mode analysis, and formulating differentiated preventive measures. Each step will be described in detail below.

[0018] In step one, multi-source fault data of the EMU air conditioning system is collected. This multi-source fault data includes two main categories: ground inspection fault data and operational fault data. Ground inspection fault data originates from automatic early warning faults from the EMU remote monitoring system and faults discovered during manual inspections during depot maintenance. Operational fault data originates from pop-up alarm faults on the HMI (Human-Machine Interface) and sudden faults discovered during onboard mechanics' inspections. The EMU type for which fault data is collected is a specific model, the collection period is year-round operational data, and the collection scope covers all fault records under multiple operating modes of the EMU air conditioning system, including cooling, heating, and ventilation modes. The collection process utilizes the EMU remote monitoring system to obtain real-time fault early warning information during operation, and simultaneously uses the maintenance operation management system to obtain manual inspection records during depot maintenance, forming a data collection system covering the entire lifecycle of the EMU air conditioning system. During data collection, the system automatically records key information such as the timestamp, train number, fault code, fault description, and fault source type for each fault data entry, ensuring data integrity and traceability.

[0019] In step two, the collected multi-source fault data is preprocessed to establish the correlation between faulty components and their sources. Preprocessing includes data cleaning, outlier removal, and fault classification standardization. The data cleaning process first verifies the integrity of the original fault data, removing missing data and duplicate records generated during data transmission or storage. The outlier removal process identifies and processes abnormal fault records caused by sensor false alarms or system errors based on preset anomaly judgment rules. The fault classification standardization process establishes a fault component coding system, uniformly mapping fault data from different sources to a standardized fault component classification table. The standardized classification table is hierarchically divided according to the components of the air conditioning system. The first level is divided into four main categories: refrigeration system components, heating system components, ventilation system components, and control system components. The second level is further subdivided into specific components such as compressors, condensers, evaporators, expansion valves, heaters, fans, ducts, temperature sensors, pressure sensors, air conditioning controllers, contactors, and relays. By establishing a unified fault component coding system, standardized processing of ground inspection fault data and operational fault data under a unified standard is achieved, laying a data foundation for subsequent statistical analysis. The process of establishing the association maps each fault record to its corresponding faulty component, and records whether the fault originated from ground inspection or operational inspection, forming a two-dimensional association matrix between the faulty component and the source of the fault.

[0020] In step three, based on the preprocessed fault data, the fault percentage of each component in different fault sources is statistically analyzed to identify overlapping components in ground inspection faults and operational faults. The statistical analysis employs a multi-dimensional fault statistical algorithm to calculate the percentage of each component in ground inspection faults, operational faults, and the overall percentage. The ground inspection fault percentage is calculated by dividing the number of faults occurring in a specific component during ground inspections by the total number of ground inspection faults; the operational fault percentage is calculated by dividing the number of faults occurring in a specific component during operation by the total number of operational faults; and the overall percentage is calculated by dividing the total number of faults occurring in a specific component throughout the year by the total number of faults throughout the year. Overlapping fault components with an overall percentage exceeding a preset threshold are selected as key analysis targets. The preset threshold is set based on the statistical analysis requirements and the total amount of fault data, typically set to components with an overall percentage exceeding 5%. Statistical analysis identifies components with a high percentage in ground inspection faults, including ductwork, auxiliary contacts, passenger compartment temperature sensors, air conditioning controllers, and electric heaters. It also identifies components with a high percentage of overlapping faults in both operational and ground inspection faults. These overlapping failure components are the key focus of subsequent failure mode analysis and prevention measure development.

[0021] In step four, based on the statistical analysis results, the correlation between overlapping fault components and seasonal environmental changes is analyzed, and the characteristics of frequently faulty components in different seasons are extracted. The seasonal correlation analysis divides the year into three periods: a winter peak period, a summer peak period, and a stable period. The winter peak period includes January and December, during which outdoor temperatures are at their lowest levels of the year, and the air conditioning system mainly operates in heating mode, significantly increasing the workload of heaters and related control components. The summer peak period includes June, July, and August, during which outdoor temperatures are at their highest levels of the year, and the air conditioning system mainly operates in cooling mode, significantly increasing the workload of compressors, condensers, and other cooling components. The stable period includes March to May in spring and September to November in autumn, during which outdoor temperatures are moderate, and the operating load of the air conditioning system is relatively stable. The failure frequency of different components in each time period is statistically analyzed, and characteristic faulty components for each peak period are extracted. Analysis results show that the failure frequency of air conditioner controllers and electric heaters increases significantly during the winter peak season, while the failure frequency of temperature sensors and contactor auxiliary contacts increases significantly during the summer peak season. Meanwhile, duct failures maintain a relatively stable frequency throughout the year. Seasonal correlation analysis reveals the inherent correlation patterns of high-incidence failure types in different seasons, providing data support for the development of differentiated preventative measures.

[0022] In step five, failure mode analysis (FMEA) is performed on the overlapping fault components, deeply analyzing the failure mechanisms of key components such as the controller, temperature sensor, contactor auxiliary contacts, and the TECH heater under specific operating conditions. The FMEA for the air conditioner controller includes relay configuration optimization analysis. The air conditioner controller integrates multiple relays to control the switching between different operating modes such as cooling, heating, and ventilation. In low-temperature environments, relay contacts are prone to condensation due to alternating hot and cold temperatures, leading to contact oxidation and poor contact. Simultaneously, the insulation performance of the relay coil decreases under low-temperature conditions, making it prone to inter-turn short circuits. Through relay configuration optimization analysis, a configuration scheme of adding redundant relays is proposed, and the relay selection is improved by choosing relay models with larger contact capacity and better insulation performance. The FMEA for the temperature sensor includes improvements to the potting process and optimization analysis of adhesive selection. Temperature sensor probes are typically sealed using a potting process. In high or low temperature environments, the adhesive is prone to aging and cracking, allowing moisture to penetrate the sensor probe, causing decreased measurement accuracy or sensor failure. Improving the potting process to enhance sealing performance and optimizing adhesive selection by choosing adhesive materials with a wider temperature range can effectively improve the environmental adaptability of temperature sensors. Failure mode analysis of contactor auxiliary contacts includes structural design improvement analysis. Contactor auxiliary contacts are used to transmit control signals and status feedback. During frequent switching, the contact surface is prone to arc erosion and mechanical wear, leading to increased contact resistance and unstable signal transmission. Improving the contact structure design to increase contact pressure and wear resistance can extend the service life of auxiliary contacts. Failure mode analysis of the electric heaters used in passenger areas includes improving fault alarm logic and early detection analysis through three-phase resistance measurement. Electric heaters are mainly used for winter heating in passenger areas. Their operating power is three-phase AC. During long-term operation, the heating wire may experience single-phase or two-phase open-circuit faults, leading to decreased heating efficiency or complete failure. Improving the fault alarm logic to identify early anomalies, such as three-phase current imbalance and reduced heating efficiency, and using three-phase resistance measurement for early detection of heater status, can eliminate faults in their early stages.

[0023] In step six, based on the failure mode analysis results, differentiated preventative measures are formulated for different seasons and different components, forming a fault analysis methodology system for the EMU air conditioning system. For the high-incidence period in winter, the relay configuration within the air conditioning controller is optimized, redundant relays are added, and their selection is improved. Specific measures include adding backup relays to the air conditioning controller, automatically switching to backup relays when the main relay fails, and replacing existing relays with new relays with higher contact capacity and better coil insulation performance. For temperature sensor failure, the potting process is improved to enhance sealing performance, and adhesive materials with a wider temperature range are selected. Specific measures include improving the temperature profile control of the potting process to ensure the adhesive fully cures without bubbles, and using modified silicone with a temperature range of -40°C to 120°C as the potting material. For contactor auxiliary contact failure, the contact structure design is improved to increase contact pressure and wear resistance. Specific measures include optimizing the contact geometry to increase the actual contact area and using a silver alloy plating material to improve wear resistance and arc resistance. To address the issue of heater failure via the air conditioning system, the fault alarm logic has been improved to identify early anomalies, and fault diagnosis can be initiated proactively by measuring three-phase resistance. Specific measures include adding a three-phase current monitoring function to the air conditioning control system, triggering an alarm when any phase current deviates from the normal range and exceeds a set threshold; and adding three-phase resistance measurement to routine maintenance, comparing the measurement results with standard values, and promptly replacing components that exceed the allowable range.

[0024] Furthermore, the method also includes establishing a continuous fault data tracking mechanism, regularly updating statistical analysis results, and dynamically adjusting the implementation parameters of differentiated preventive measures to form a closed-loop optimized fault analysis method. The continuous tracking mechanism includes regularly collecting and updating fault data of the EMU air conditioning system, typically on a monthly or quarterly basis. Updates include new fault records and corrections to historical fault data. Regular updates of statistical analysis results ensure that the analysis conclusions reflect the latest fault distribution status, and the dynamic adjustment mechanism optimizes and adjusts the implementation parameters of various preventive measures based on the updated statistical analysis results. The closed-loop optimization process forms a complete cycle of problem identification, problem analysis, measure formulation, measure implementation, effect evaluation, and continuous improvement, enabling the fault analysis method system to continuously adapt to actual field needs and changes in the external environment.

[0025] The following provides a specific application example to further illustrate the method of this invention. A certain EMU depot is responsible for the year-round maintenance of a certain type of EMU. This type of EMU consists of eight motor cars and eight trailer cars, totaling sixteen cars. Each motor car and trailer car is equipped with an independent air conditioning system. In implementing the method of this invention, firstly, automatic early warning fault data during the year's operation is collected through the EMU remote monitoring system. Simultaneously, fault data discovered during manual inspections during maintenance is collected through the maintenance operation management system. A total of over 18,000 fault records of various types are collected throughout the year. After preprocessing the collected fault data, the correlation between faulty components and fault sources is established. Through statistical analysis, four overlapping fault components with a combined proportion exceeding five percent are identified: air conditioning controller, temperature sensor, contactor auxiliary contacts, and electric heater. Further seasonal correlation analysis identified January and December as the peak failure periods in winter, during which air conditioning controller failures accounted for 28% of the annual total, and failures via the electric heaters accounted for 32%. June to August was the peak summer failure period, during which temperature sensor failures accounted for 35% of the annual total, and contactor auxiliary contact failures accounted for 23%. Based on the failure mode analysis results, an optimized configuration scheme for air conditioning controller relays was developed for the winter peak period, an improved potting process scheme for temperature sensors was developed for the summer peak period, a structural design improvement scheme was developed for the contactor auxiliary contacts (a high-risk area throughout the year), and a scheme to improve fault alarm logic and proactively investigate three-phase resistance measurements for failures via the electric heaters was developed. After implementing these differentiated preventative measures and a complete operational cycle of tracking and verification, the overall failure rate of the EMU air conditioning system decreased by approximately 40% compared to before implementation. Specifically, air conditioning controller failures decreased by 45%, temperature sensor failures decreased by 38%, contactor auxiliary contact failures decreased by 42%, and failures via the electric heaters decreased by 50%. Passenger comfort and EMU operational order were significantly improved.

[0026] Example 2 To further illustrate the implementation of the method of the present invention in different application scenarios, another specific embodiment is provided below.

[0027] In another embodiment, the fault analysis method for the air conditioning system of high-speed trains is applied to a certain type of standard high-speed train, which adopts a four-motor, four-trailer eight-car formation and is equipped with a distributed air conditioning system. During the data acquisition phase, in addition to collecting fault data as described in Embodiment 1, a mechanism for data exchange with adjacent train depots is added. Fault data of similar high-speed trains in adjacent depots are obtained through data interfaces, further expanding the data sample size and improving the representativeness of the statistical analysis results. In the preprocessing phase, in addition to data cleaning, outlier removal, and fault classification standardization, a data quality assessment function is added. This function quantitatively evaluates indicators such as data integrity, data consistency, and data timeliness to ensure that the data entering the statistical analysis phase meets the preset quality requirements. The statistical analysis phase employs an adaptive threshold setting algorithm, dynamically adjusting the screening threshold for overlapping fault components based on the distribution characteristics of the fault data to avoid analytical bias caused by fixed threshold settings. In the seasonal correlation analysis phase, in addition to monthly fault frequency statistics, a fine-grained analysis mode based on ten-day periods and weeks is added, enabling more accurate identification of high-incidence fault characteristics during seasonal transitions and extreme weather periods. The failure mode analysis phase established a component failure mode database, classifying and storing the failure modes of various components under different operating conditions to form a queryable and updatable knowledge base system, providing support for new employee training and technology transfer. The preventative action development phase established a predictive model for the effectiveness of preventative measures, quantitatively predicting the expected effects after the implementation of various preventative measures, providing a basis for resource allocation and prioritization decisions.

[0028] This embodiment further improves the fault analysis method system for EMU air conditioning systems by expanding the data collection scope, adding data quality management, introducing adaptive analysis algorithms, establishing a failure mode knowledge base, and constructing an effect prediction model, thereby enhancing the applicability, reliability, and intelligence level of the method.

[0029] Example 3 The following provides an implementation of the method of the present invention in one application scenario.

[0030] In another embodiment, the fault analysis method for EMU air conditioning systems is applied to the development of fault analysis capabilities in newly built EMU depots. Newly built EMU depots lack historical fault data accumulation in the early stages of operation, making it difficult to obtain effective analytical conclusions by directly using statistical analysis methods. To address this specific situation, the method of this invention adds a baseline data import step before step one. The data sources for the baseline data import step include historical fault data from other mature EMU depots, component reliability data provided by equipment suppliers, and component failure rate data specified in industry standards. After standardization, the imported baseline data is fused and analyzed with the actual fault data collected by the newly built EMU depot. Even with limited data, valuable analytical conclusions can still be obtained. As the operating time of the newly built EMU depot extends, the amount of actual fault data collected gradually increases, and the confidence level of the analytical conclusions also improves, ultimately achieving independent analysis based entirely on the actual data of the depot itself.

[0031] Furthermore, this embodiment incorporates a tiered implementation strategy in the preventative measures implementation phase. Based on factors such as implementation difficulty, resource consumption, and expected results, the measures are categorized into three levels: emergency implementation, planned implementation, and observation implementation. Emergency implementation measures include optimization projects with extremely high failure rates and low handling costs; planned implementation measures include improvement projects with relatively high failure rates and requiring certain resource investment; and observation implementation measures include forward-looking research projects with moderate failure rates and whose implementation effects are yet to be verified. This tiered implementation strategy ensures that key measures are prioritized when resources are limited, while also reserving room for continuous improvement.

[0032] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A fault analysis method for an air conditioning system of a high-speed train, characterized in that, The process includes the following steps: Step 1, collecting multi-source fault data of the EMU air conditioning system. This multi-source fault data includes ground inspection fault data and operational fault data. The ground inspection fault data includes automatic early warning faults from the EMU remote monitoring system and faults discovered during manual inspections upon entering the depot for maintenance. The operational fault data includes HMI pop-up alarm faults and sudden faults discovered during onboard mechanic inspections. Step 2, preprocessing the collected multi-source fault data to establish the correlation between faulty components and fault sources. Step 3, based on the preprocessed fault data, statistically analyzing the fault percentage of each component in different fault sources, and identifying overlapping fault components in ground inspection and operational faults. Step 4, based on the statistical analysis results, analyzing the correlation between overlapping fault components and seasonal environmental changes, and extracting the characteristics of high-incidence fault components in different seasons. Step 5, conducting failure mode analysis for each overlapping fault component, deeply analyzing the failure mechanisms of controllers, temperature sensors, contactor auxiliary contacts, and electric heaters under specific operating conditions. Step 6, based on the failure mode analysis results, formulating differentiated preventative measures for different seasons and different components, forming a fault analysis methodology system for EMU air conditioning systems.

2. The fault analysis method for the air conditioning system of a high-speed train according to claim 1, characterized in that, The type of EMU for which fault data is collected in step one is a certain type of EMU, the collection period is the whole year of operation data, and the collection scope covers all fault records of the EMU's air conditioning system under multiple working modes such as cooling mode, heating mode, and ventilation mode.

3. The fault analysis method for the air conditioning system of a high-speed train according to claim 1, characterized in that, The preprocessing operations in step two include data cleaning, outlier removal, and fault classification standardization. A fault component coding system is established to uniformly map fault data from different sources to a standardized fault component classification table.

4. The fault analysis method for the air conditioning system of a high-speed train according to claim 1, characterized in that, In step three, the statistical analysis uses a multidimensional fault statistics algorithm to calculate the proportion of each component in ground inspection faults, the proportion in operational faults, and the overall proportion. Components with overlapping faults whose overall proportion exceeds a preset threshold are selected as key analysis objects.

5. The fault analysis method for the air conditioning system of a high-speed train according to claim 1, characterized in that, In step four, the seasonal correlation analysis divides the year into three periods: a winter peak period, a summer peak period, and a stable period. The failure frequency of different components in each period is statistically analyzed, and characteristic failure components of each peak period are extracted. The winter peak period includes January and December, and the summer peak period includes June, July, and August.

6. The fault analysis method for the air conditioning system of a high-speed train according to claim 1, characterized in that, The failure mode analysis of the air conditioner controller in step five includes relay configuration optimization analysis, failure mode analysis of the temperature sensor includes potting process improvement and adhesive selection optimization analysis, failure mode analysis of the contactor auxiliary contacts includes structural design improvement analysis, and failure mode analysis of the Taidian heater includes fault alarm logic improvement and three-phase resistance measurement for early troubleshooting analysis.

7. The fault analysis method for the air conditioning system of a high-speed train according to claim 1, characterized in that, The differentiated preventive measures in step six include: optimizing the relay configuration in the air conditioner controller for the high-incidence period in winter, increasing redundant relays and improving their selection; improving the potting process to enhance sealing performance and selecting adhesive materials with a wider temperature range to address temperature sensor failure; improving the contact structure design to increase contact pressure and wear resistance to address contactor auxiliary contact failure; and refining the fault alarm logic to identify early anomalies and detect faults in advance by measuring three-phase resistance. The method also includes establishing a continuous fault data tracking mechanism, regularly updating statistical analysis results, dynamically adjusting the implementation parameters of differentiated preventive measures, and forming a closed-loop optimized fault analysis method.

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