Data acquisition and quality management and control system for railway wagon axle detection

Abstract

The invention relates to the technical field of mechanical engineering and intelligent detection, discloses a data acquisition and quality management and control system for railway wagon axle detection, and aims to solve the problems that in the prior art, manual recording is prone to mistakes and omissions, data acquisition and quality judgment are disjointed, and closed-loop management and control cannot be achieved. The system comprises a commercial digimatic micrometer additionally provided with an external Bluetooth device, a portable small flat plate and a quality management system, a wireless transmission link is constructed through Bluetooth, and static size data is transmitted to the small tablet computer in real time and uploaded to the system; the quality system is additionally provided with a data receiving, checking and storing module, process standards are automatically compared, and abnormal data are visually identified. According to the technical scheme, automatic collection, real-time verification and abnormity early warning of high-precision static size data can be achieved, and the detection accuracy, efficiency and quality tracing ability are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of mechanical engineering and intelligent detection technology, specifically relating to a data acquisition and quality control system for railway freight car axle detection. Background Technology

[0002] With the rapid development of the railway transportation industry, railway freight car axles, as key load-bearing and transmission components, are directly affected by the dimensional accuracy of their manufacturing and maintenance processes, which in turn impacts operational safety and service life. High-precision testing of the static geometric dimensions of critical components such as axle journals, dust covers, and wheel seats during the final inspection and maintenance stages of axle manufacturing is a core quality control step to ensure that axles meet process standards.

[0003] Currently, axle dimension inspection largely relies on manual operation of digital micrometers for measurement, with data imported into the quality management system via paper records or manual entry. This method is not only inefficient but also highly susceptible to human error, leading to data errors, omissions, or unit confusion, severely impacting the accuracy and traceability of quality assessments. While some solutions attempt to introduce automated sensing technologies, such as laser dynamic diameter measurement systems, these are primarily suitable for online inspection scenarios during operation and cannot meet the specific requirements of offline, static, and high-precision dimensional measurement in the final manufacturing inspection stage. Furthermore, health monitoring methods based on acoustic emission or vibration analysis focus on dynamic fault modes such as cracks and fatigue, failing to cover quality control of static geometric parameters.

[0004] Existing technologies in the field of static dimensional quality inspection of railway freight car axles generally suffer from a disconnect between inspection methods and business processes, and a separation between data collection and quality judgment. On the one hand, while commercially available high-precision digital micrometers possess excellent metrological performance, they lack seamless integration with information systems. On the other hand, although quality management systems have data storage capabilities, they lack real-time standard comparison, format verification, and anomaly visualization mechanisms, resulting in the inability to immediately transform inspection data into effective quality decision-making basis. These deficiencies keep axle dimensional quality inspection in an inefficient "collection-entry-post-verification" mode, making it difficult to achieve closed-loop control from data collection to quality judgment to anomaly early warning. Therefore, there is an urgent need for a data collection and quality control system that integrates high-precision handheld measuring tools and intelligent quality systems for final manufacturing inspection scenarios. Summary of the Invention

[0005] The purpose of this invention is to provide a data acquisition and quality control system for railway freight car axle detection, which can effectively solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A data acquisition and quality control system for railway freight car axle inspection includes the following specific modules: Step (1) Constructing a handheld static dimension data wireless acquisition unit: An external Bluetooth device is installed on a commercial digital micrometer with data acquisition function, so that the external Bluetooth device and the digital micrometer establish a stable electrical connection, and pair with a portable tablet through the Bluetooth communication protocol to form a wireless data transmission link from the digital micrometer through the external Bluetooth device to the tablet; Step (2) Configuring a mobile data relay and system access terminal: After the tablet completes pairing with the external Bluetooth device, it automatically starts the pre-installed quality management system client. The operator logs into the system through identity authentication, making the tablet a data relay node at the inspection site, receiving and caching the axle dimension measurement values ​​from the digital micrometer in real time; Step (3) Establishing a quality system intelligent verification and storage mechanism: The quality system adds a data receiving module, a data verification module and a data storage module, wherein the data receiving module is used to receive the measurement values ​​uploaded by the tablet, and the data verification module... The system has a pre-stored database of process standards for different types of railway freight car axles, including the standard range and data format specifications for key dimensions such as journal diameter, dust cover diameter, and wheel seat diameter. When the data is received, the data verification module performs data format compliance verification and numerical range comparison in sequence. If the format does not match, a reminder instruction is triggered. If the value exceeds the standard range, it is marked as abnormal data. Step (4) realizes the visualization identification and associated storage of abnormal data: The data storage module automatically associates the data that meets the standard with the unique identification information of the corresponding axle and stores it in the database according to the verification results. At the same time, the abnormal data is given a visualization identification attribute when it is stored, so that the data is presented in red font in the system interface, which is convenient for quality management personnel to quickly identify. Step (5) supports multi-station mobile operation and closed-loop quality feedback: The small tablet maintains Bluetooth connection and data upload when the inspection personnel move between different verification stations to ensure the continuity of data throughout the process. The system automatically generates a quality inspection report containing abnormal items based on the abnormal identification, which is used for subsequent review or maintenance decision-making.

[0008] Preferably, the digital micrometer in step (1) is a Mitutoyo brand digital micrometer, whose output interface conforms to the RS232 standard protocol. The external Bluetooth device is connected to the interface through a dedicated adapter cable. There is no need to modify the micrometer body structure. It has strong compatibility, and the cost of a single modification is significantly lower than the deployment cost of the laser diameter measurement system, and the deployment cycle is greatly shortened.

[0009] Preferably, in step (2), the small tablet is an industrial-grade tablet device that is lightweight and has a long battery life. The screen size is adapted to the on-site operation requirements, supports a high protection level, ensures stable operation in the dusty and vibrating environment of the railway freight car maintenance workshop, and supports long-term multi-station mobile inspection operations.

[0010] Preferably, in step (3), the process standard database of the data verification module is stored according to the axle model. Each category contains multiple key dimension parameters. Each parameter has upper and lower tolerance zones. The tolerance zone width is determined according to the relevant railway freight car axle manufacturing technical conditions. The data format requirements include uniformity of numerical type, decimal places and units. The system verification response time meets the real-time requirements.

[0011] Preferably, the visualization in step (4) is not limited to red font, but also includes highlighting abnormal rows in the axle list view, generating a heat map of abnormal numbers in the statistics dashboard, and supporting filtering of abnormal data by axle number, inspection date, operator, etc., to improve the efficiency of quality traceability.

[0012] Preferably, in step (5), the automatic quality inspection report generation module automatically generates a structured report based on the saved data after each inspection task is completed. The report content includes basic axle information, measured values ​​of each inspection point, standard range, deviation value and abnormal identification status. The report format conforms to the railway industry quality document specifications and can be directly exported as PDF or printed for archiving.

[0013] Preferably, the external Bluetooth device adopts the Bluetooth Low Energy protocol, the communication distance meets the requirements of on-site operation, the data transmission rate ensures signal stability when the inspection personnel move around the vehicle axle, the packet loss rate is controlled at an extremely low level, and it supports one-to-many connection, allowing multiple micrometers to be connected to the same small tablet in turn.

[0014] Preferably, the quality system is deployed on the enterprise's intranet server and supports interface with the railway freight car manufacturing execution system (MES). It synchronizes the axle inspection results to the production work order in real time, realizing the linkage between quality data and production process. Abnormal axle information can automatically trigger the generation of rework work orders.

[0015] Preferably, this system is suitable for offline, static, and high-precision dimensional quality inspection scenarios in the final inspection stage of railway freight car axle manufacturing. It is clearly distinguished from dynamic or non-geometric parameter detection scenarios such as operation status monitoring, non-destructive testing, and online vehicle maintenance, and focuses on the diameter dimension control of key parts such as journals, dust covers, and wheel seats.

[0016] Preferred results show that, based on actual production line comparison tests, the system significantly reduces the error rate of axle size data entry, significantly improves the efficiency of single axle inspection, greatly shortens the average inspection time, and reduces rework losses caused by data errors annually.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] 1. Achieve automated acquisition and closed-loop management of high-precision static dimensional data: By using an external Bluetooth device to adapt to commercial high-precision digital micrometers, wireless automatic acquisition of handheld static dimensional data can be achieved without modifying the measuring instrument itself, solving the problem of errors and omissions in traditional manual recording. Combined with the real-time standard comparison and anomaly visualization identification functions built into the quality system, the inefficient "collection-entry-post-verification" mode is upgraded to a closed-loop quality control process of "collection-verification-identification-feedback", significantly improving data accuracy and decision-making timeliness.

[0019] 2. Adapting to Specific Business Scenarios of Final Inspection in Railway Freight Car Manufacturing: This system is explicitly limited to offline, static, high-precision dimensional quality inspection scenarios, complementing rather than replacing dynamic operation monitoring or non-destructive testing technologies. Supporting multi-station mobile operations via a lightweight tablet, it perfectly matches the actual working conditions of dispersed inspection stations and frequent personnel movement in maintenance workshops, ensuring the continuity and integrity of data collection and avoiding process interruptions due to insufficient equipment portability.

[0020] 3. Significantly Improved Detection Efficiency and Economic Benefits: Experimental data shows that this solution significantly reduces data error rates and improves detection efficiency compared to traditional manual data entry. Compared to embedded solutions such as laser dynamic diameter measurement, the "external Bluetooth + commercial micrometer" combination significantly reduces single-point deployment costs and shortens the deployment cycle while ensuring high measurement accuracy, greatly lowering the threshold for enterprise intelligent transformation and making it suitable for large-scale promotion.

[0021] 4. Construct a multi-level quality early warning and traceability system. Through red-font identification of abnormal data, heat map display, and automatic generation of quality inspection reports, quality management personnel can quickly locate problematic axles without having to check each one individually. The system supports multi-dimensional filtering and traceability, and abnormal data is automatically associated with the axle's unique identifier, operator, and inspection time, providing a complete data chain for quality analysis and responsibility determination, and strengthening the controllability of quality throughout the entire process. Attached Figure Description Figure 1 This is a schematic diagram of the overall technical architecture of the data acquisition and quality control system for railway freight car axle detection proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the wireless data acquisition and verification closed loop constructed based on an external Bluetooth device and a digital micrometer in this invention. Figure 3 This is a flowchart illustrating the logical process of data acquisition, transmission, and system access in a multi-station mobile operation within this invention. Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow between the abnormal data visualization identification, associated storage and quality feedback in this invention;

[0022] Figure 5This is a schematic diagram of the adapter cable in this invention. Detailed Implementation

[0023] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0024] Example 1

[0025] In the final inspection stage of railway freight car axle manufacturing, the axle needs to undergo high-precision static geometric dimension inspection of key components such as axle journals, dust covers, and wheel seats offline. In the traditional operation mode, inspectors use commercial digital micrometers with data acquisition capabilities to measure each part of the axle point by point, manually recording the readings on paper forms or entering them into the quality management system via keyboard. This process is not only time-consuming but also highly susceptible to human error, leading to data errors such as incorrect or missing entries, unit confusion, or decimal place errors, severely impacting subsequent quality judgment and traceability efficiency. To address these issues, this embodiment constructs a dedicated data acquisition and quality control system for the final inspection scenario of railway freight car axle manufacturing. Its core lies in achieving wireless automatic acquisition, real-time verification, visual identification of anomalies, and closed-loop feedback of high-precision static dimensional data.

[0026] The system first constructs a handheld static dimensional data wireless acquisition unit at the hardware level. Specifically, a Mitutoyo digital micrometer is selected as the basic measuring tool. This model of micrometer is widely used in the railway industry, possessing a resolution of 0.001 mm and a repeatability of ±2 micrometers. Its output interface conforms to the RS232 standard protocol. An external Bluetooth device establishes a stable electrical connection with this RS232 interface via a dedicated adapter cable. This adapter cable integrates a level conversion circuit to ensure reliable conversion between TTL level signals and RS232 levels, while also providing power isolation and signal filtering functions to suppress the impact of electromagnetic interference in the workshop environment on communication stability. The external Bluetooth device adopts the Bluetooth Low Energy 5.0 protocol, has a built-in 200 mAh rechargeable lithium battery, supports continuous operation for more than 8 hours, and has a communication distance of up to 15 meters in open environments. Even in the actual working conditions of a maintenance workshop with multiple metal structures providing cover, it can still maintain an effective communication radius of more than 10 meters. The data transmission rate is set to 9600 baud, ensuring a stable transmission of one set of measurement values ​​per second. The packet loss rate has been measured and controlled below 0.1%. This external Bluetooth device requires no structural modification to the micrometer itself; installation can be completed simply by physical clamping and electrical interface connection. The cost of a single modification is kept below RMB 800, far lower than the deployment cost of tens of thousands of RMB for laser diameter measurement systems, and on-site deployment can be completed within 10 minutes.

[0027] Detailed design of the level conversion and isolation filter circuit:

[0028] To achieve reliable conversion and isolation between RS232 and TTL levels, the dedicated adapter cable integrates a level conversion circuit, preferably using the TD301D232H serial port conversion chip as its core. This chip is specifically designed for bidirectional conversion between RS232 and TTL / CMOS levels, supports full-duplex communication with a maximum speed of 115200bps, and has a built-in electrostatic discharge (ESD) protection unit, effectively resisting instantaneous high voltages generated by friction during maintenance and protecting the precision interface circuitry of the digital micrometer.

[0029] The power isolation circuit is implemented using a DC / DC isolated power supply module (such as F0505S-2WR3). This module provides electrical isolation strength of up to 1500VDC between input and output, completely blocking the "ground loop current" caused by the difference in ground potential of multiple devices on site, preventing common-mode noise from entering the measurement signal side, and ensuring the purity and stability of the digital micrometer measurement reference.

[0030] The signal filtering circuit described is designed to address broadband electromagnetic interference generated by equipment such as welding machines and trolley motors in railway maintenance workshops. The circuit employs a multi-stage composite filtering architecture, connecting ferrite beads in series and filter capacitors in parallel along the signal path to form an LC and RC filter network. Its effective suppression frequency range covers 150kHz to 30MHz, focusing on filtering out switching power supply noise, motor commutation sparks, and radio frequency interference within this band. After this filtering process, the digital signal edges are clear, significantly reducing the risk of data errors and packet loss in Bluetooth transmission.

[0031] After the handheld data acquisition unit is constructed, a mobile data relay and system access terminal is configured. This terminal is an industrial-grade portable tablet, weighing no more than 600 grams, with an 8-inch screen, IP65 protection rating (resisting dust intrusion and light water splashes), and a built-in 8000mAh battery supporting 12 hours of continuous operation, meeting the needs of multi-station mobile inspection during a single shift. The tablet comes pre-installed with a customized quality management system client, which automatically scans and attempts to establish a connection with paired external Bluetooth devices after system startup. Once the inspector completes the initial pairing of the external Bluetooth device with the tablet, it automatically reconnects on each subsequent power-on, eliminating the need for repeated operations. After the operator completes authentication by entering their employee ID and dynamic password, the system automatically loads the list of axle inspection tasks within their authorized scope. During the inspection process, after each measurement is completed, the digital micrometer's internal microcontroller sends a raw data frame containing the measured value, timestamp, and gauge ID to an external Bluetooth device via the RS232 interface. The external Bluetooth device parses the data frame, encapsulates it into a data packet conforming to a custom application layer protocol, and pushes it to a tablet PC in real time via the Bluetooth link. Upon receiving the data packet, the tablet PC immediately stores it in its local cache and displays the current measured value, the corresponding axle position (e.g., "journal - left end"), the standard tolerance zone (e.g., "Φ130.000±0.025mm"), and the real-time deviation value on the user interface, ensuring that inspectors can immediately confirm the validity of the measurement results.

[0032] The tablet acts as a data relay node, uploading cached measurement data in real time to the quality management system deployed on the intranet server via the enterprise's intranet Wi-Fi or 4G private network. The system adds three core functional modules to the original architecture: a data receiving module, a data verification module, and a data storage module. The data receiving module listens on a designated TCP port, receiving structured JSON data from the tablet. This data structure includes fields such as: axle unique identifier (e.g., "AXLE-20240515-0017"), operator's employee number, inspection time (accurate to milliseconds), measurement part code, measured value, gauge serial number, and data source device ID. The data receiving module performs integrity verification on the data packets (e.g., CRC32 check). If the verification fails, it returns a retransmission command to the tablet.

[0033] The data verification module is the core of this system for achieving intelligent quality control. This module integrates a process standard database, which is categorized and stored according to railway freight car axle models (such as C70, P70, NX70, etc.). Each axle type has multiple key dimensional parameters, each containing the following attributes: standard nominal size (unit: mm), upper deviation (unit: mm), lower deviation (unit: mm), allowable decimal places (usually 3), value type (floating-point), and unit (uniformly mm). For example, for the journal diameter of a C70 freight car axle, its standard nominal size is 130.000, upper deviation is +0.025, and lower deviation is -0.025, therefore the acceptable range is [129.975, 130.025]. When the data receiving module successfully receives a set of data, it immediately triggers the data verification module to execute a two-level verification process. The first level is data format compliance verification: the system checks whether the measured value is a valid floating-point number, whether the number of decimal places exceeds 3, and whether the unit is missing, etc. If the format is incorrect (e.g., receiving "130.0001" or "130mm"), the system immediately generates a format error alert and pushes it to the tablet client via the message queue, forcibly popping up a prompt box requiring the operator to remeasure or correct. The second level is numerical range comparison and verification: the system queries the corresponding model based on the axle's unique identifier, then locates the specific parameter item based on the measurement part code, and compares the measured value with the upper and lower limits of that parameter item. If the measured value is within the [lower limit, upper limit] range, it is considered qualified data; if it exceeds this range, it is marked as abnormal data, and the direction of deviation (too large or too small) and the absolute deviation value are recorded.

[0034] The data storage module executes a differentiated storage strategy based on the verification results. For qualified data, the system automatically associates it with metadata such as the axle's unique identifier, operator, inspection time, and gauge information, and writes it into the main table of the relational database. The table structure design ensures data atomicity and consistency. For abnormal data, in addition to performing the above association storage, a boolean field "is" is additionally set in the database record. anomaly "True" is true, and a string field "visual" is appended. flag The value assigned is "red". font This field is directly accessed by the front-end interface, causing all abnormal data to be automatically displayed in red font when viewed by the system. Furthermore, in the axle list view, the system highlights the entire row of axles containing at least one abnormal data item; in the quality statistics dashboard, it generates heatmaps of the number of abnormalities by day, week, and month, with color intensity reflecting the frequency of abnormalities; it also supports filtering abnormal data by multiple dimensions such as axle number, inspection date, operator, and axle model, greatly improving the efficiency of quality traceability.

[0035] In actual operation, inspectors need to move between multiple calibration stations to inspect different axles. This system ensures continuous data flow throughout the entire process through the continuous network capability of the tablet and the stable connection of the external Bluetooth device. Even if the system briefly enters a signal dead zone during movement, the tablet will temporarily store the data in a local SQLite database and automatically synchronize it to the server after the network is restored, avoiding data loss. After each inspection task is completed (i.e., all key parts of an axle have been measured), the system automatically triggers the quality inspection report generation module. This module reads all inspection records for the axle and generates a structured PDF report, which includes: basic axle information (number, model, manufacturing batch), measured values ​​of each inspection point, corresponding standard ranges, calculated deviation values, anomaly status (normal / abnormal), and the operator's signature. The report template strictly follows the quality document specifications in the "Rules for Assembly, Repair and Management of Railway Freight Car Axles" and can be directly printed for archiving or distributed to review engineers via intranet email.

[0036] Furthermore, the anomaly heatmap in the quality statistics dashboard is generated not only by simple counting statistics, but also by embedded intelligent analysis logic based on multi-dimensional attribute clustering. This aims to reveal the distribution patterns and potential root causes of abnormal data from spatial, temporal, and feature dimensions. The specific implementation of this clustering logic is as follows: The system periodically (e.g., every two hours) extracts all recent (e.g., the last 30 days) anomaly records from the database's anomaly data table. Each record contains attributes: axle model, measurement location (e.g., journal, dust cover, wheel seat), anomaly deviation direction (larger or smaller), absolute value of deviation, inspection station number, operator ID, and inspection timestamp (accurate to the hour). The data preprocessing module first standardizes and vectorizes these attributes, performs one-hot encoding on categorical variables (e.g., axle model, location), converts timestamps into a combination of "day of the week - shift (e.g., day shift / night shift)," and normalizes numerical deviations.

[0037] Subsequently, the system invokes its built-in clustering analysis engine (e.g., using the density-based DBSCAN algorithm or an improved K-means algorithm) to calculate the multidimensional similarity between data points using the processed feature vectors as input. The algorithm's core parameters (such as neighborhood radius and minimum number of points) are adaptively adjusted based on historical data distribution. The goal of the clustering engine is to discover anomalous data point groups that are tightly clustered in the "model-location-time-personnel" feature space. Each identified cluster is considered to represent a potential, systematic anomalous pattern. For example, the algorithm might identify a significant cluster characterized by "P70 axle," "left end journal," "large deviation direction," "concentrated on Tuesday night shifts," and "high correlation with operator A." This clustering result indicates a potential risk of systematic dimensional oversize due to specific personnel operating habits, the influence of nighttime ambient temperature and humidity, or inherent deviations in the batch of blanks.

[0038] The heatmap generation module enhances the coloring logic of traditional two-dimensional matrices (dimensions typically "date" and "axle type" or "workstation") based on clustering results. The basic heatmap layer still uses the anomaly count of cells to determine the color depth (e.g., light yellow to dark red). The enhanced layer overlays cluster markers: for data points belonging to the same high-density anomaly cluster, their corresponding heatmap cells will be overlaid with specific patterns (e.g., diagonal shading) or highlighted borders. When the user hovers over the cell, the tooltip will not only display the number of anomalies but also list the main clustering patterns associated with them, such as "The P70 axle neck anomaly is concentrated on this day, suspected to be related to the night shift operation of workstation #03." In addition, the system provides an independent "Anomaly Cluster Analysis" view, which displays all identified significant clusters and their core characteristics, the number of axles involved, and the time span in the form of a list or network diagram. It also supports one-click drill-down to view all specific anomaly records within a cluster, thereby elevating quality control from post-event statistics to the level of pre-event pattern discovery and root cause tracing, greatly assisting quality engineers in making targeted process adjustments or training interventions.

[0039] Furthermore, this system supports deep integration with the Manufacturing Execution System (MES) for railway freight cars. When the quality system determines that an axle has abnormal data, it automatically pushes the abnormal event to the MES system via the API interface. Based on this, the MES system automatically generates a rework order, assigns it to the designated maintenance team, and locks the axle's flow status in the production process until it passes a retest. This linkage mechanism achieves closed-loop control of quality data and the production process, eliminating the risk of defective axles flowing into the next process.

[0040] After a three-month comparative test at the final inspection line of the C70 model at a large railway freight car manufacturer, the system reduced the axle dimension data entry error rate from 1.8% in the traditional manual mode to 0.02%, and shortened the average inspection time per axle from 8.5 minutes to 5.2 minutes, improving inspection efficiency by 38.8%. This reduces rework losses due to data errors by approximately RMB 420,000 annually. Furthermore, the single-point deployment cost is only 12% of the laser diameter measurement solution, and the deployment cycle is shortened from two weeks to one day, significantly lowering the barrier to intelligent transformation for enterprises.

[0041] Example 2

[0042] Based on Example 1, this example constructs a universal data acquisition adaptation architecture compatible with multiple source measuring tools to address the differences in interface protocols among different brands of digital micrometers, in order to solve the system compatibility problem for enterprises when upgrading equipment or using measuring tools from different brands.

[0043] In this embodiment, the external Bluetooth device is no longer limited to Mitutoyo micrometers that only support the RS232 protocol, but has been upgraded to a smart adapter with multi-protocol parsing capabilities. This adapter integrates a programmable microcontroller (such as the STM32F4 series), whose firmware supports dynamically loading communication protocol parsing modules for different measuring instrument brands. Currently, the system has pre-installed communication protocol libraries for mainstream brands such as Mitutoyo, Mahr, and TESA, covering multiple interface standards including RS232, USB HID, and Digimatic. When a new measuring instrument is connected, the operator selects the brand and model of the measuring instrument through a small tablet client. The system automatically downloads the corresponding protocol parsing module from the server and burns it into the external Bluetooth device. This device automatically matches the correct parsing algorithm by recognizing features such as the data frame header, checksum, and end marker of the measuring instrument's output, thereby correctly extracting the measurement value.

[0044] In the data verification module, the process standard database has been expanded to support dynamic tolerance zone configuration. For new axle models or prototype products, quality engineers can temporarily define new dimensional parameters and tolerance ranges through the system management backend, and the system will take effect immediately without downtime. Furthermore, the data verification logic incorporates historical data trend analysis. The system not only determines whether a single measurement value exceeds the tolerance, but also combines historical measurement data of the same part of the axle (such as the average journal diameter of the previous 10 axles in the same batch) to calculate the Z-score of the current value.

[0045] in, This is the current measured value. This is the historical average. This represents the historical standard deviation. If |Z| > 3, even if the current value is still within the tolerance zone, the system will mark it as a "potential anomaly" and display it in yellow on the interface for quality engineers to focus on, enabling early quality warnings.

[0046] In terms of abnormal data visualization, this embodiment adds a three-dimensional spatial mapping function. For measurement points with axial distribution characteristics, such as wheel seats, the system constructs a radial runout curve from the diameter data of multiple axial positions and displays it on the three-dimensional model. Abnormal points are highlighted in red, making it easy to intuitively judge axle roundness or taper problems.

[0047] Example 3

[0048] This embodiment focuses on the automated guidance and error prevention mechanism of the inspection process, and is suitable for military or export-type railway freight car axle inspection scenarios with extremely high requirements for operational standardization.

[0049] In this embodiment, the tablet client integrates a testing process navigation engine. The system automatically loads a preset testing sequence and a list of mandatory test points based on the axle model (e.g., "test the left journal first, then the right dust mount..."). Operators must strictly follow the sequence to complete the measurements. The system uses dual verification via the external Bluetooth device's gauge ID and measurement location code to ensure no critical points are skipped. If the operator attempts to measure a location not specified in the current step, the system will refuse to receive data and issue a voice prompt.

[0050] The data verification module has been enhanced to include logical consistency verification. For example, the difference in diameter between the left and right wheel seats of the same axle must not exceed 0.05mm. If the difference between two measurements exceeds this threshold, even if both are within the tolerance zone, the system will still determine it as a logical anomaly and generate a composite anomaly flag. Furthermore, the system records the settling time for each measurement—that is, the time it takes for the micrometer reading to remain stable within ±0.001mm. If the settling time is less than 1 second, it is considered an improper measurement operation, the data is marked as "suspicious," and a retest is required.

[0051] The quality inspection report generation module has added electronic signature and blockchain evidence storage functions. Operators, reviewers, and quality supervisors must sign their names by hand on a small tablet in turn. The system writes the report hash value into the company's private blockchain to ensure that the inspection records cannot be tampered with and to meet the audit requirements for export products.

[0052] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A data acquisition and quality control system for railway freight car axle inspection, characterized in that, The system includes a handheld wireless static dimensional data acquisition unit, a mobile data relay and system access terminal, and a quality management system deployed on an enterprise intranet server. The handheld wireless static dimensional data acquisition unit consists of a commercial digital micrometer with data acquisition capabilities, an external Bluetooth device, and a dedicated adapter cable. The external Bluetooth device establishes an electrical connection with the output interface of the digital micrometer via the dedicated adapter cable and pairs with the mobile data relay and system access terminal based on the Bluetooth communication protocol, forming a wireless data transmission link from the digital micrometer through the external Bluetooth device to the mobile data relay and system access terminal. The mobile data relay and system access terminal is an industrial-grade portable tablet pre-installed with the quality management system client. After pairing with the external Bluetooth device, the client automatically starts. After the operator logs in through authentication, the tablet acts as a data relay node at the inspection site, receiving and caching axle dimension measurement values ​​from the digital micrometer in real time. The quality management system includes a data receiving module and a data calibration module. The system comprises a verification module and a data storage module. The data receiving module receives measurement values ​​uploaded by a small tablet. The data verification module pre-stores a process standard database categorized by axle model, including key dimensional standard ranges and data format specifications for axle journal diameter, dust cover diameter, and wheel seat diameter. It sequentially performs data format compliance checks and value range comparisons on the received data. If the format is inconsistent, a warning instruction is triggered; if the value exceeds the standard range, it is marked as abnormal data. The data storage module automatically associates compliant data with the corresponding axle's unique identifier and stores it in the database based on the verification results. Abnormal data is given a visual identifier attribute during storage, making it appear in red font on the system interface. The system supports Bluetooth connectivity and data upload for inspection personnel moving between multiple workstations and automatically generates quality inspection reports containing abnormal items based on the abnormal identifiers. The quality management system interfaces with the Railway Freight Car Manufacturing Execution System (MES) via an API interface. When abnormal data is detected in an axle, it uses the API... The interface sends a lock command to the MES containing a unique axle identifier and an anomaly code. The MES then intercepts the work order passing request corresponding to the identifier until it receives a retest pass release command from the quality management system.

2. The data acquisition and quality control system for railway freight car axle inspection according to claim 1, characterized in that, The output interface of the digital micrometer conforms to the RS232 standard protocol. The external Bluetooth device is connected to the interface through a dedicated adapter cable. The dedicated adapter cable integrates a level conversion circuit, a power isolation circuit, and a signal filtering circuit, without requiring any modification to the micrometer's structure.

3. The data acquisition and quality control system for railway freight car axle inspection according to claim 1, characterized in that, The mobile data relay and system access terminal is an industrial-grade tablet device weighing no more than 600 grams, with an 8-inch screen and an IP65 protection rating. Its built-in battery supports continuous operation for more than 12 hours and is suitable for multi-station mobile inspection operations in dusty and vibrating environments in railway freight car maintenance workshops.

4. The data acquisition and quality control system for railway freight car axle inspection according to claim 1, characterized in that, The process standard database in the data verification module configures multiple key dimensional parameters for each axle model. Each parameter has upper and lower tolerance zones, which are determined based on the manufacturing technology conditions of railway freight car axles. The data format specifications limit the numerical type to floating point, the number of decimal places to three, and the unit to millimeters.

5. The data acquisition and quality control system for railway freight car axle inspection according to claim 1, characterized in that, In addition to displaying abnormal data in red, the visualization identification attribute is also configured to highlight the entire row of records containing abnormal data in the axle list view, generate a heatmap of the number of abnormalities by day, week or month in the quality statistics dashboard, and support filtering of abnormal data by axle number, inspection date or operator.

6. The data acquisition and quality control system for railway freight car axle inspection according to claim 1, characterized in that, After all key parts of a single axle have been measured, the automatic quality inspection report generation module generates a structured report based on the saved test data. The report includes basic axle information, measured values ​​at each test point, corresponding standard ranges, deviation values, and abnormal status indicators. The report format conforms to the railway industry quality document specifications and can be exported as PDF or printed for archiving.

7. The data acquisition and quality control system for railway freight car axle inspection according to claim 1, characterized in that, The external Bluetooth device adopts the Bluetooth Low Energy 5.0 protocol, with a communication distance of no less than 10 meters in the maintenance workshop environment, a data transmission rate set to 9600 baud rate, a packet loss rate of less than 0.1%, and supports a one-to-many connection mode, allowing multiple micrometers to be connected to the same small tablet in turn.

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