Gate seat type braking system based on cloud sound spectrum technology

Abstract

The invention relates to a gate seat type brake system based on a cloud sound spectrum technology, which comprises a data acquisition unit used for acquiring brake audio of the brake system through a plurality of sound collectors; the comparison unit is used for establishing a standard curve database and comparing a brake sound curve; the iteration unit is used for iteratively updating the standard curve database, and the data acquisition unit continuously acquires sound data; each section of sound data synchronously records auxiliary working condition variables such as time, temperature, wind speed, load and the like, a database is established by comparing standard curves of the units, and specific sound is restored by using an energy and density conjoint analysis method; establishing a unique standard sound spectrum for a brake system or model of each crane, automatically retaining records with optimal performance and strongest stability in N days in a standard library through an iteration unit, and periodically optimizing a standard curve; the standard curve can be adjusted along with time and is used for reflecting the natural wear trend and improving the early warning precision.

Description

Technical Field

[0001] This invention relates to the field of gantry crane technology, and in particular to a gantry braking system based on cloud sound spectrum technology. Background Technology

[0002] With the deepening of intelligent manufacturing, ports and large logistics facilities are placing higher demands on equipment operating efficiency, operational safety, and intelligent maintenance capabilities. As one of the core pieces of equipment in port loading and unloading systems, gantry cranes are widely used in the handling and transfer of containers, bulk cargo, and heavy equipment, and their operational reliability has become a key factor in ensuring port throughput capacity.

[0003] Among the many core subsystems of a gantry crane, the braking system plays a crucial role as a "safety valve." This system is not only used for start-stop control during daily operation, but also undertakes critical tasks such as forced braking and safety locking in emergency situations. It operates frequently, under high loads, and in complex conditions; a failure could not only damage the equipment but also pose serious safety hazards and the risk of port operation disruptions.

[0004] I. Common Braking System Faults and Potential Hazards: Under long-term, high-intensity use, the braking system of gantry cranes is prone to the following faults: Electromagnetic synchronization failure: Due to asynchronous operation or delayed response of the electromagnets, the brakes may fail to release or close in a timely manner; Brake pad wear: Long-term friction leads to insufficient brake pad thickness, affecting braking torque; Intermittent impact noise: Jamming, loosening, or installation deviation in the brake linkage mechanism generates impact vibration during operation; Abnormal friction noise: Surface contamination or uneven structure leads to abnormal friction noise, which is a signal of poor contact between the brake pads and the wheel disc. All of these abnormalities can be effectively warned through early acoustic signal capture and identification.

[0005] The industry currently uses several types of testing methods, but all of them have obvious shortcomings.

[0006] For example, manual periodic inspections are one of the most common maintenance methods. Typically, on-site operators or maintenance engineers conduct visual inspections and functional tests on the braking system during equipment downtime or periodic shutdowns. This includes observing brake pad wear, testing electromagnet engagement, and identifying any abnormal noises. Technical characteristics: Relies on human senses (hearing, seeing, touching) to judge the braking system's operating status; requires experienced maintenance workers to determine if there are problems such as uneven friction, mechanism misalignment, or electromagnet failure; mostly periodic checks with long intervals. Problems: High subjectivity: Inspection results heavily rely on personal experience and judgment, lacking consistency; Many blind spots: Non-exposed components such as the inside of the brake cylinder and the worn surface of the brake pads are difficult to observe accurately; Delayed response: If a sudden fault occurs during the inspection interval, there is a risk of missed detection and failure; High labor costs: High-frequency inspections significantly increase maintenance costs and are easily affected by personnel turnover; Unquantifiable: Cannot generate continuous data records, making trend analysis and intelligent prediction difficult.

[0007] 2. Electrical Monitoring System: This type of system typically monitors the operating parameters (such as current, voltage, temperature, and action time) of electrical components in the braking system, such as electromagnetic coils, motor controllers, and PLCs, in real time to determine if there are any abnormalities in the control system. Technical Features: It can collect the operating status of the electrical control system in real time, such as whether the brake electromagnet is energized and whether the engagement time is abnormal; it has good remote monitoring and alarm functions; and it is easy to integrate into the overall machine electrical control system. Problems: It can only identify electrical faults, such as control signal loss, power supply abnormalities, and coil open circuits; however, it cannot monitor mechanical problems (such as friction noise and mechanical jamming); indirect judgment is limited: although current or action delay may indirectly reflect brake faults, there is a lack of direct physical response signals as support; information granularity is insufficient: it cannot distinguish the health status of individual components in the braking system; it is susceptible to electrical interference: misjudgments or signal loss are prone to occur in complex electromagnetic environments; it is difficult to judge acoustic problems: such as the "stepping out of step" sound emitted by the electromagnet, and electromagnetic tooth knocking cannot be reflected by current and voltage.

[0008] 3. Vibration Analysis and Thermal Imaging: This technology is primarily based on the vibration and thermal radiation characteristics generated by equipment during operation. It utilizes sensors for signal acquisition and pattern analysis, and is widely used for health monitoring of rotating machinery and heavy-duty equipment. Technical features: Vibration Analysis: By installing accelerometers (vibration meters), the frequency response and acceleration characteristics of the equipment during operation are monitored to identify problems such as structural loosening, resonance, and fatigue. Thermal Imaging Detection: Infrared thermal imagers are used to detect the surface temperature distribution of the braking system, identifying faults caused by uneven friction and brake overheating. Problems include: High detection threshold: Vibration analysis has poor response to intermittent, low-amplitude vibration changes in braking systems and is only suitable for continuously rotating structures; Blurred thermal imaging judgment mechanism: Brake heating has different temperature rise rates under different operating conditions, and high temperature does not necessarily indicate an anomaly, which can easily lead to misjudgment; Difficulty in identifying initial faults: Minor wear, mechanical jamming, and other initial problems often do not manifest in vibration and thermal signals; Complex sensor installation: High-precision vibration sensors need to be installed close to critical locations, which is difficult due to the limitations of gate structure wiring and placement; High cost and difficult maintenance: Precision thermal imaging and vibration acquisition systems are generally expensive and have high maintenance costs, making them unsuitable for large-scale routine deployment.

[0009] Therefore, it is particularly necessary to study a high-precision, intelligent braking system fault detection scheme with acoustic recognition capabilities. Summary of the Invention

[0010] The purpose of this invention is to provide a gantry-type braking system based on cloud sound spectrum technology to solve the problems existing in the prior art.

[0011] The above technical objectives of the present invention are achieved through the following technical solutions: A gantry braking system based on cloud-based acoustic spectrum technology includes: The data acquisition unit is used to acquire brake audio from the braking system through multiple sound acquisition devices; The comparison unit is used to establish a standard curve database and compare brake sound curves; An iterative unit is used to iteratively update the standard curve database within the unit.

[0012] By adopting the above technical solution, the data acquisition unit continuously collects sound data of the gantry crane's braking system under different environments, loads, and operating frequencies; each sound data segment synchronously records auxiliary operating condition variables such as time, temperature, wind speed, and load. The comparison unit uses a standard curve database and a joint energy and density analysis method to reconstruct the specific sound; a unique standard sound spectrum is established for each crane's braking system or model. Through an iterative unit, the standard library automatically retains the records with the best performance and strongest stability over N days, periodically optimizing the standard curve; the standard curve can be adjusted over time to reflect natural wear trends and improve early warning accuracy.

[0013] In a further embodiment, a braking system life warning unit is established, and the comparison unit performs clustering and fitting analysis on the collected data every three days; it tracks the energy changes of the main frequency and high frequency bands and constructs a time-feature trend curve.

[0014] By adopting the above technical solutions, tracking and analyzing brake audio data, and comparing data, the remaining service life of the brakes can be predicted based on the sliding state, which facilitates the establishment of a sound maintenance mechanism.

[0015] In a further embodiment, the method for establishing the brake sound curve is as follows: Step 1: Noise Reduction. Query the energy values ​​at the start and end points of each audio segment, set the time period T (in seconds), and set the audio time to S seconds. Therefore, T data points can be obtained, where T = S / T. Step 2: Read the intersection point of each period T with the highest point of the audio energy, and record it as the peak and valley value of the audio energy within this period T; Step 3: Composite curve. The peak and valley values ​​are fitted into multiple curves using Lagrange interpolation. These multiple curves are then processed into a single curve, which is labeled P1. This curve is then compared with the standard curve.

[0016] By adopting the above technical solution, assuming the period is set to 1 second and the audio is 30 seconds long, it is divided into 30 segments. The data in each second is directly aggregated to the end value, resulting in 31 data points for the 0th second, 1st second, 2nd second...30th second, which means there are 31 peak and valley values. By fitting a curve with five consecutive data points, and this curve needs to be fitted with at least three data points, 26 curves can be obtained. Six curves are randomly selected at equal intervals, and these six curves must include the starting curve. Then, the ends of these six curves are fitted to obtain a complete curve. The reason for randomly selecting six curves at equal intervals is that there are a total of 31 data points, and fitting is done once for every five data points, so six curves with equal intervals are needed to include all the peak and valley values ​​on the curve.

[0017] In a further embodiment, a data filtering system is provided to select the audio energy curve during the braking process. The data filtering system is used to filter the monotonically increasing interval on the curve P1.

[0018] By adopting the above technical solution, in a braking system, the sound energy level generated by braking should decrease monotonically. Eventually, as the speed drops to zero, the sound energy generated by the friction between the brake and the brake disc becomes zero. Therefore, all monotonically increasing intervals indicate the presence of risks. These risk areas need to be compared and analyzed to better maintain the braking system.

[0019] In a further embodiment, the data in the monotonically increasing interval on the curve P1 is marked as abnormal data, and the abnormal data is compared and removed by historical data.

[0020] By adopting the above technical solution, some anomalies will only have a temporary impact on the braking system, so it is necessary to remove the data in this part.

[0021] In a further embodiment, an anomaly feature database is established.

[0022] In a further embodiment, the abnormal feature database consists of energy level curves of the sound emitted by the brake disc during braking when objects of different volumes are placed between the brake and the brake disc.

[0023] By adopting the above technical solution, this database is established based on the hard objects that will appear in actual use. The main hard object is sand, so the database is mainly for sand and gravel.

[0024] In a further embodiment, temperature data is detected, and the standard curve is adjusted based on the temperature data.

[0025] By adopting the above technical solution, the temperature data here refers to the ambient temperature, the brake pad temperature, and the brake disc temperature. These three temperatures all affect the friction between the brake pads and the brake disc. Therefore, it is necessary to use these three data points in combination with historical data to fine-tune the standard curve in real time, making it more flexible in distinguishing between different conditions.

[0026] In summary, the present invention has the following beneficial effects: 1. The data acquisition unit continuously collects sound data of the gantry crane's braking system under different environments, loads, and operating frequencies. Each sound data segment synchronously records auxiliary operating condition variables such as time, temperature, wind speed, and load. A database of standard curves from the comparison unit is built, and a joint energy and density analysis method is used to reconstruct the specific sound. A unique standard sound spectrum is established for each crane's braking system or model. Through an iterative unit, the standard library automatically retains the best-performing and most stable records from the past N days, periodically optimizing the standard curves. The standard curves can be adjusted over time to reflect natural wear trends and improve the accuracy of early warning systems. Attached Figure Description

[0027] Figure 1 This is a structural schematic diagram illustrating the braking system of the present invention; Figure 2 This is a flowchart used to illustrate the overall process of this invention; Figure 3 This is a flowchart illustrating the method for establishing curve P1 in this invention; Figure 4 This is a flowchart illustrating the data iteration method of the present invention; Figure 5 This is a graph used to illustrate the data filtering system of this invention. Detailed Implementation

[0028] The present invention will be further described in detail below with reference to the accompanying drawings.

[0029] Identical parts are indicated by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "upper," and "lower" used in the following description refer to the attached figures. Figure 1 In this specification, the terms "bottom surface" and "top surface," "inner" and "outer" refer to the direction toward or away from the geometry of a specific component. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this specification, "a plurality of" means two or more, unless otherwise explicitly and specifically defined by the direction of the center.

[0030] Example 1: like Figures 1-4 As shown, the gantry braking system based on cloud sound spectrum technology includes: The data acquisition unit is used to acquire brake audio from the braking system through multiple sound acquisition devices; The comparison unit is used to establish a standard curve database and compare brake sound curves; An iterative unit, which is used to iteratively update the standard curve database within the unit by comparing it with the data. The braking system life warning unit and comparison unit perform clustering and fitting analysis on the collected data every three days; track the energy changes of the main frequency and high frequency bands, and construct time-feature trend curves; An abnormal feature database is established, which consists of energy level curves of the sound emitted by the brake disc during braking when objects of different volumes are placed between the brake and the brake disc. Temperature data is detected, and the standard curve is adjusted based on this data.

[0031] Specific implementation process: The braking system tracks and analyzes braking audio data, comparing changes in braking audio at different locations to facilitate the establishment of a comprehensive maintenance mechanism. Multiple sound acquisition devices are set up at the motor, gear squeaking device, small gear squeaking device, and metal rubbing device to collect audio data at each location. First, the motor's audio is collected. Multiple qualified motors are sampled to generate an audio segment. Based on the audio curve, the audio energy tends to be balanced before 1.7s, then begins to increase after 1.7s, fluctuates significantly before 4.3s, and then gradually flattens out after 4.3s. Therefore, the audio segment from 1.7s to 4.3s is selected for acquisition. This segment is then divided into zones with a period of 0.1, resulting in 27 data points. These data are then further divided into 5... Continuous data points are fitted using Lagrange interpolation to create a curve that passes through 80% of the data points. This is because three consecutive data points can yield too many curves, and even four consecutive data points may result in multiple fitted curves. Only five or more data points provide a truly unique curve. With 27 data points, fitting five curves is insufficient to fit all data points; at least two data points will not be fitted. Therefore, an additional curve is needed, resulting in six curves. The ends of these six curves are then fitted to create a single curve that passes through 80% of the 27 data points. Comparing the collected sound energy data reveals a high degree of overlap in the qualified motor sound data curves. The reason the second peak is higher than subsequent audio data is that during motor startup, the rotor speed increases from 0, resulting in the maximum slip (the difference between the rotating magnetic field speed and the rotor speed), a significant increase in rotor current, and intensified electromagnetic force pulsation between the stator and rotor, leading to stronger electromagnetic noise. During stable operation, the slip is smaller, electromagnetic force pulsation weakens, and electromagnetic noise decreases.

[0032] Next, sound was collected from the sibilant device, resulting in an audio segment. Based on the audio curve, the audio energy tends to balance before 1.96s, then begins to increase after 1.96s, fluctuates significantly before 2.54s, and then gradually flattens out after 2.54s. Therefore, the audio segment from 1.96s to 2.54s was selected for collection. This segment was then divided into sections with a period of 0.02, resulting in 30 data points. Five consecutive data points were then used to fit a curve using Lagrange interpolation, ensuring the fitted curve passed through 80% of the data points, resulting in six curves. Finally, the ends of these six curves were fitted to create a single curve. The line passes through 80% of the data points of 30 data points, and then the collected sound curve is compared with qualified equipment to achieve precise quantification of low-frequency subtle sounds, thereby judging whether the gear noise equipment is qualified; the reason why the second peak data is greater than the subsequent audio data is that the gear will be subjected to a large inertial impact force at the moment of starting from a standstill and starting to rotate. The driving gear suddenly drives the driven gear to rotate, and the inertia of the driven part will react on the gear, causing the tooth surface to bear instantaneous impact load. This accelerated impact will make the contact position of the gear meshing tooth surface unstable, which may deviate from the ideal tooth surface center and deviate towards the tooth tip or tooth root, resulting in a reduction of the tooth surface contact area and stress concentration, causing additional vibration noise. When the speed stabilizes, the impact force of gear meshing tends to be stable, and the noise decreases accordingly; Next, sound was collected from the small sibilant device, resulting in an audio segment. According to the audio curve, the audio energy tends to be balanced before 1.984s, then shows an increasing trend after 1.984s, fluctuating significantly before 2.026s, and then gradually leveling off after 2.026s. Therefore, the audio segment from 1.984s to 2.026s was selected for collection. This segment was then divided into zones with a period of 0.002, resulting in 22 data points. Five consecutive data points were then used to fit a curve using Lagrange interpolation. Since four fitting curves cannot cover all data points, additional curves were used. One more curve is needed to fit five curves, ensuring that each curve passes through 80% of the data points. Then, the ends of these five curves are fitted to create a single curve that passes through 80% of the 22 data points. The collected sound curve is then compared with a qualified gear-whistling device. The difference in a very small frequency deviation (0.483 Hz) is used to determine if the device is qualified. The reason the second peak value is greater than subsequent audio data is that when the device is stationary, the lubricating oil on the gear meshing surface gradually settles or accumulates at the bottom due to gravity, and the oil film on the gear surface may become thinner or even partially missing. At startup, the gear tooth surface is in a "semi-dry friction" or "boundary lubrication" state, increasing the frictional force of direct contact between the metal tooth surfaces. This significantly increases the friction and impact noise during meshing. As the device operates, the lubricating oil is carried to the tooth surface by the gear meshing action, forming a complete oil film, reducing direct friction, and thus lowering the noise. Sound was collected from the iron-grinding device, resulting in an audio segment. Based on the audio curve, the audio energy tends to be balanced before 3.5 seconds, then begins to increase after 4.3 seconds, with more drastic fluctuations before 3.5 seconds, and then gradually leveling off after 4.3 seconds. Therefore, the audio segment from 3.5 seconds to 4.3 seconds was selected for collection. This segment was then divided into zones with a period of 0.05, resulting in 17 data points. Five consecutive data points were used to fit a curve using Lagrange interpolation, ensuring the fitted curve passed through 80% of the data points, resulting in four curves. These four curves were then fitted at their ends to create a single curve that passed through 80% of the 17 data points. The collected sound curve was then compared with that of qualified equipment to achieve precise quantification of low-frequency, subtle sounds, thereby determining whether the grating device was up to standard. Every mechanical system has its inherent resonant frequency. The reason why the second peak data is greater than the subsequent audio data is that when the device starts up, the speed gradually increases from 0 and briefly passes through a speed close to the system's natural frequency. At this time, the device will resonate, causing the vibration amplitude to increase sharply and the noise to be amplified. When the speed stabilizes and moves away from the resonant frequency, the vibration and noise will be significantly reduced.

[0033] Changes in the sound of the braking system are important signals reflecting its operating status. When braking is initiated, the friction between the brake pads and brake discs generates sound, and the change in sound energy is directly related to the vehicle's deceleration process. Under normal circumstances, as the car's speed gradually decreases, the relative speed between the brake pads and brake discs slows down, the intensity of the vibration generated by friction weakens, and therefore the sound energy shows a monotonically decreasing trend. This decreasing process continues until the vehicle comes to a complete stop, at which point friction disappears, and the sound energy drops to zero.

[0034] If the sound sensor detects a monotonically increasing range of sound energy, it indicates an abnormal risk in the braking system. This could be due to uneven brake pad wear, scratches on the brake disc surface, or an abnormal contact angle between the brake pads and the brake disc, causing the friction intensity to increase during braking. This abnormal friction not only reduces braking efficiency but may also accelerate component wear and even lead to serious malfunctions such as brake jamming or overheating. It is essential to promptly alert the system and conduct an inspection.

[0035] From the perspective of variation patterns, the sound energy of normal braking gradually decreases exponentially. In real-world scenarios, due to factors such as road slope and braking force, the sound energy change curve may be fitted by the superposition of multiple exponential functions, but the core characteristic remains an overall decrease. The coefficient K of the exponential function of a qualified braking system will be stable within a specific range. When the coefficient K of the exponential function of a braking system is detected to be less than that of a qualified braking system, it indicates that the sound energy decreases too slowly. This reflects signs of aging such as decreased brake pad hardness and reduced brake disc surface smoothness. In this case, the braking efficiency of the braking system has significantly deteriorated, and continued use may lead to increased braking distance. This is a warning sign that the system is about to be scrapped, and new brake components need to be replaced in time to ensure driving safety.

[0036] However, in actual operation of the braking system, hard objects such as sand and gravel often intrude between the brake and the brake disc. This can interfere with the normal operation of the brake and cause different effects due to the different sizes of hard objects. It is difficult to accurately identify the abnormality by manual judgment alone. Therefore, it is necessary to build an abnormal feature database through systematic data accumulation to provide a reliable basis for brake abnormality detection.

[0037] The database is constructed primarily using sand and gravel as the research object, simulating various real-world scenarios through experiments. First, sand and gravel samples of different volume ranges are divided. Under the premise of controlling the stability of variables such as braking force and vehicle speed, these sand and gravel samples are placed between the brake and the brake disc, and the energy level curves of the sound emitted by the brake disc during braking are collected. Simultaneously, the influence of ambient temperature, brake pad temperature, and brake disc temperature on friction is considered. The correlation between these temperature factors and the sound energy curve is analyzed through experiments, and then an adjustment model is constructed based on historical data to dynamically optimize the standard curve. Furthermore, abnormal cases from actual vehicle operation will be continuously added, and the database will be updated regularly to include characteristic curves under special operating conditions.

[0038] Databases can provide accurate diagnostic criteria for brake system anomaly detection. Using feature curves within the database, it's possible to quickly identify whether hard objects such as sand or gravel have intruded during braking, and the approximate nature of these objects. Furthermore, dynamic adjustments based on temperature factors allow the identification results to better reflect actual operating conditions, enhancing the flexibility and accuracy of detection. This continuously improves the ability to identify various anomalies, providing strong support for the stable operation of the brake system and driving safety.

[0039] In the embodiments disclosed in this invention, the terms "installation," "connection," "linking," and "fixing" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; "linking" can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the embodiments disclosed in this invention according to the specific circumstances.

[0040] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of the claims of the present invention.

Claims

1. A gantry-type braking system based on cloud-based acoustic spectrum technology, characterized in that: include: The data acquisition unit is used to acquire brake audio from the braking system through multiple sound acquisition devices; The comparison unit is used to establish a standard curve database and compare brake sound curves; An iterative unit is used to iteratively update the standard curve database within the unit.

2. The gantry braking system based on cloud sound spectrum technology according to claim 1, characterized in that: A braking system lifespan early warning unit was established, and the comparison unit performed clustering and fitting analysis on the collected data every three days; the energy changes of the main frequency and high frequency bands were tracked, and time-feature trend curves were constructed.

3. The gantry braking system based on cloud sound spectrum technology according to claim 1, characterized in that: The method for establishing the brake sound curve is as follows: Step 1: Noise Reduction. Query the energy values ​​at the start and end points of each audio segment, set the time period T (in seconds), and set the audio time to S seconds. Therefore, T data points can be obtained, where T = S / T. Step 2: Read the intersection point of each period T with the highest point of the audio energy, and record it as the peak and valley value of the audio energy within this period T; Step 3: Composite curve. The peak and valley values ​​are fitted into multiple curves using Lagrange interpolation. These multiple curves are then processed into a single curve, which is labeled P1. This curve is then compared with the standard curve.

4. The gantry braking system based on cloud sound spectrum technology according to claim 3, characterized in that: A data filtering system is set up to select the audio energy curve during the braking process. The data filtering system is used to filter the monotonically increasing interval on curve P1.

5. The gantry braking system based on cloud sound spectrum technology according to claim 4, characterized in that: Data in the monotonically increasing intervals on curve P1 are marked as abnormal data, and abnormal data are compared and removed by historical data.

6. The gantry braking system based on cloud sound spectrum technology according to claim 1, characterized in that: Establish an anomaly feature database.

7. The gantry braking system based on cloud sound spectrum technology according to claim 6, characterized in that: The abnormal feature database consists of energy level curves of the sound emitted by the brake disc during braking when objects of different sizes are placed between the brake and the brake disc.

8. The gantry braking system based on cloud sound spectrum technology according to claim 1, characterized in that: It also includes detecting temperature data and adjusting the standard curve based on the temperature data.

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