A method and system for detecting the amount of iron passing through a hydraulic jaw crusher

Abstract

The application belongs to the technical field of foreign matter detection in crushing equipment, and discloses a hydraulic jaw crusher over-iron amount detection method and system. The method comprises the following steps: presetting a detection test wave, the detection test wave being sent in a specific area of the equipment and being propagated; presetting a plurality of wear state partitions, establishing a preset baseline feature library corresponding to each wear state partition, and each wear state partition corresponding to each wear state of the equipment; receiving a response signal after the propagation of the detection test wave; analyzing the response signal, obtaining a modulation response after the propagation of the detection test wave; judging the current wear state partition, obtaining a preset baseline feature library corresponding to the current wear state partition, comparing the preset baseline feature library and the modulation response, and judging whether there is metal foreign matter, so that small-size metal foreign matter can be effectively identified, and the cumulative damage to the crusher can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of foreign matter detection in crushing equipment, in particular to a hydraulic jaw crusher over-iron detection method and system. BACKGROUND

[0002] As a key equipment in the mineral processing flow, the hydraulic jaw crusher is mainly used for primary crushing of large bulk ore. Its working principle is to realize crushing by extruding and bending the material entering the crushing cavity through the periodic movement of the moving jaw and the fixed jaw. In actual production process, various metal foreign matters such as waste mechanical parts, reinforcing bars, bolts, etc. are often mixed in the raw materials. If these metal foreign matters cannot be effectively identified and discharged, they will cause serious cumulative damage to the crusher, affecting the normal operation and service life of the equipment. Although the hydraulic jaw crusher usually has an overload protection function, allowing the moving jaw to retreat to discharge foreign matters when encountering excessive resistance, metal foreign matters, especially those with large size or irregular shape, may still cause damage to the key components such as the jaw plate during passing through the crushing cavity.

[0003] In production lines with extremely high requirements for product purity or extremely low tolerance of metal foreign matters by downstream equipment, even tiny metal particles may cause serious consequences. In such specific application scenarios, the traditional over-iron detection method based on pressure peak value of the hydraulic system or sudden change of motor current often lacks sufficient sensitivity and resolution, making it difficult to effectively identify small-size metal foreign matters. The hydraulic or current fluctuations caused by these small-size foreign matters are relatively small, and are easily covered by the signal fluctuations generated when crushing large or hard ores, leading to missed detection.

[0004] As a consumable part, the surface topography of the jaw plate will wear out with the use time, the tooth shape will become blunt, and the tooth height will decrease. When new jaw plates and worn jaw plates interact with the material (including metal foreign matters), their mechanical response characteristics will be different. When the degree of wear of the jaw plate changes greatly, the detection performance fluctuates greatly throughout the service life of the jaw plate, bringing uncertainty to production management and equipment maintenance.

[0005] In summary, the existing hydraulic jaw crusher has low detection accuracy for metal foreign matters, and is prone to missed detection and false detection, which needs to be improved. SUMMARY

[0006] The purpose of the present application is to provide a hydraulic jaw crusher over-iron detection method and system, which can effectively identify small-size metal foreign matters and reduce the cumulative damage to the crusher.

[0007] In one aspect, the present application provides a hydraulic jaw crusher over-iron detection method, and the technical solution is as follows:

[0008] The method comprises: presetting a probe test wave, the probe test wave being transmitted in a device-specific region and propagating; presetting a plurality of wear state partitions, establishing a preset baseline feature library corresponding to each wear state partition, each wear state partition corresponding to a wear state of the device; receiving a response signal after the propagation of the probe test wave; analyzing the response signal to obtain a modulation response after the propagation of the probe test wave; determining the current wear state partition, obtaining the preset baseline feature library corresponding to the current wear state partition, comparing the preset baseline feature library and the modulation response, and determining whether there is a metal foreign object.

[0009] Through the above scheme, the metal foreign object can be effectively identified by analyzing the propagation characteristics of the probe test wave in the device, the cumulative damage to the crusher is reduced, and the parameters of identification are adjusted to reduce the missed identification and misidentification caused by the wear of the e-plate.

[0010] Further, the application also proposes that the establishment of the preset baseline feature library corresponding to each wear state partition comprises: establishing a preset baseline feature library corresponding to each wear state partition, the preset baseline feature library comprising a normal baseline feature library and a metal modulation feature library; determining the current wear state partition, obtaining the modulation response generated under normal production state as a normal baseline, analyzing the statistical tolerance range of the normal baseline, and establishing the normal baseline feature library; determining the current wear state partition, obtaining the modulation response affected by the mechanical disturbance device arranged on the device as a first group of modulation feature signals, the mechanical disturbance device applying mechanical disturbance to a specific region of the device to simulate the effect of metal contact with the e-plate of the device; obtaining the response signal affected by the metal placed on the e-plate of the device as a second group of modulation feature signals; combining the first group of modulation feature signals and the second group of modulation signals to generate a metal baseline, and establishing the metal modulation feature library corresponding to the current wear state partition; obtaining the preset baseline feature library corresponding to the current wear state partition, comparing the preset baseline feature library and the modulation response, and determining whether there is a metal foreign object, which comprises: when it is detected that the modulation response deviates from the statistical tolerance range and matches any metal baseline feature in the metal modulation feature library, it is determined that there is a metal foreign object.

[0011] Through the above scheme, the metal foreign object can be more accurately identified, and misjudgment can be reduced.

[0012] Further, the application also proposes that the current wear state partition is judged, a plurality of groups of the modulation response generated under normal production state are obtained as normal baseline, statistical tolerance range of the normal baseline is analyzed, and a normal baseline feature library is established, including: judging the current wear state partition, and obtaining a plurality of groups of the modulation response generated under normal production state as the normal baseline; analyzing non-steady state changes or atypical fluctuation sections of the normal baseline, obtaining the statistical tolerance range of the normal baseline under the current wear state partition according to the non-steady state changes or atypical fluctuation sections, and establishing the normal baseline feature library.

[0013] Through the above scheme, the accuracy and reliability of detection can be improved.

[0014] Further, the application also proposes that the non-steady state changes or atypical fluctuation sections of the normal baseline are analyzed, the statistical tolerance range of the normal baseline under the current wear state partition is obtained according to the non-steady state changes or atypical fluctuation sections, and the normal baseline feature library is established, including: presetting atypical fluctuation judgment basis corresponding to each wear state partition respectively; selecting corresponding atypical fluctuation judgment basis according to the current wear state partition, and analyzing the normal baseline according to the corresponding atypical fluctuation judgment basis to identify non-steady state changes or atypical fluctuation sections of the normal baseline, the atypical fluctuation judgment basis being used to define non-typical fluctuation of a test wave caused by non-steady state changes of a feeding state or instantaneous passing of non-metallic hard materials under a corresponding wear state.

[0015] Through the above scheme, atypical fluctuation can be more accurately identified, and misjudgment can be avoided.

[0016] Further, the application also proposes that the current wear state partition is judged, including: receiving cumulative working time and / or processing material quantity of the equipment, and judging the current wear state partition through preset judgment rules.

[0017] Through the above scheme, the wear state partition can be accurately judged according to the working state of the equipment.

[0018] Further, the application also proposes that the current wear state partition is judged, including: monitoring transition parameter drift of the modulation response in a transition process of two adjacent wear state partitions under normal production state, establishing a wear interval baseline library, and the wear interval baseline library containing a baseline fluctuation range corresponding to any wear state partition; when the fluctuation of the modulation response deviates from the baseline fluctuation range corresponding to the current wear state partition under normal production state, it is judged that the next level wear state partition is entered.

[0019] Through the above scheme, the wear state can be monitored in real time, and the detection parameters can be adjusted in time.

[0020] Further, the application also proposes that the corresponding non-typical fluctuation judgment basis is selected according to the current wear state partition, and the normal baseline is analyzed according to the corresponding non-typical fluctuation judgment basis to identify the non-steady state change or non-typical fluctuation section of the normal baseline, including: monitoring at least one operating parameter of the equipment, the operating parameter being related to the material currently processed by the equipment or the non-jaw plate component working condition; judging the current material type or the non-jaw plate component working condition according to the operating parameter; under normal production conditions, if the fluctuation of the response signal after the operating parameter changes is the non-typical fluctuation judgment basis; if the fluctuation of the response signal deviates from the baseline fluctuation range corresponding to the current wear state partition under normal production conditions, it is judged to enter the next level of the wear state partition, and further comprising: obtaining the corresponding non-typical fluctuation judgment basis according to the current operating parameter and the wear state partition; judging whether the fluctuation of the current response signal meets the current non-typical fluctuation judgment basis; if the fluctuation of the current response signal meets the current non-typical fluctuation judgment basis, it is judged to remain in the current wear state partition; if the fluctuation of the current response signal does not meet the current non-typical fluctuation judgment basis, and the fluctuation of the current response signal deviates from the baseline fluctuation range corresponding to the current wear state partition, it is judged to enter the next level of the wear state partition.

[0021] Through the above scheme, the judgment basis can be dynamically adjusted according to the operating parameter, and the adaptability of detection can be improved.

[0022] Further, the application also proposes that a preset test wave is preset, and the test wave is sent in a specific area of the equipment and propagates, including: determining preset parameters of the test wave, the preset parameters including frequency characteristics and morphological characteristics of an excitation signal; selecting the frequency characteristics based on the structure, material and environmental noise characteristics of the equipment when working; setting the morphological characteristics of the test wave based on the selected frequency characteristics.

[0023] Through the above scheme, the parameters of the test wave can be optimized, and the sensitivity of detection can be improved.

[0024] Further, the application further provides that the analysis of the response signal obtains the modulation response after the propagation of the probe test wave, comprising: obtaining at least one working condition parameter related to the current processing material type, particle size distribution and / or moisture content of the device; adjusting the method or parameter of analyzing the frequency characteristics or time domain waveform features of the signal component related to the probe test wave in the response signal according to the at least one working condition parameter; using the adjusted method or parameter to analyze the signal component related to the probe test wave in the response signal to obtain the frequency characteristics or time domain waveform features, the method or parameter comprising an algorithm for extracting the frequency characteristics or time domain waveform features of the signal component, filter parameters, threshold settings and / or feature calculation window; analyzing the frequency characteristics or time domain waveform features of the response signal, comparing and analyzing the modulation characteristics and / or disturbance characteristics in the preset baseline feature library under the current wear state partition, separating the signal component of the modulation information related to the probe test wave, and taking the signal component as the modulation response.

[0025] Through the above scheme, the analysis method can be dynamically adjusted according to the working condition parameters, and the detection accuracy is improved.

[0026] On the other hand, the application further provides a hydraulic jaw crusher over-iron amount detection system for detecting metal foreign matter in a hydraulic jaw crusher, comprising: an excitation source module for generating a probe test wave to a specific area of the device; a receiving sensor module for receiving a response signal after the propagation of the probe test wave; a plurality of preset wear state partitions, a preset baseline feature library corresponding to each wear state partition is established, and each wear state partition corresponds to a wear state of the device; an analysis module for analyzing the response signal and obtaining the modulation response after the propagation of the probe test wave; a judgment module for judging the current wear state partition, obtaining the preset baseline feature library corresponding to the current wear state partition, comparing the preset baseline feature library and the modulation response, and judging whether there is metal foreign matter.

[0027] Through the above scheme, automatic detection of metal foreign matter can be realized, and production efficiency is improved.

[0028] As can be seen from the above, the hydraulic jaw crusher over-iron amount detection method and system provided by the application can effectively identify small-size metal foreign matter by presetting a probe test wave and analyzing its response signal, combining wear state partitions and a preset baseline feature library, and reducing the cumulative damage to the crusher. The application has the advantages of being able to effectively identify small-size metal foreign matter and reduce the cumulative damage to the crusher. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1is a flowchart of a method for detecting the amount of iron passing through a hydraulic jaw crusher according to an embodiment of the present application.

[0030] Figure 2 is a flowchart of a method for detecting the amount of iron passing through a hydraulic jaw crusher according to another embodiment of the present application.

[0031] Figure 3 is a flowchart of a method for detecting the amount of iron passing through a hydraulic jaw crusher according to another embodiment of the present application.

[0032] Figure 4 is a flowchart of a method for detecting the amount of iron passing through a hydraulic jaw crusher according to another embodiment of the present application.

[0033] Figure 5 is a flowchart of a method for detecting the amount of iron passing through a hydraulic jaw crusher according to another embodiment of the present application.

[0034] Figure 6 is a flowchart of a method for detecting the amount of iron passing through a hydraulic jaw crusher according to another embodiment of the present application.

[0035] Figure 7 is a flowchart of a method for detecting the amount of iron passing through a hydraulic jaw crusher according to another embodiment of the present application.

[0036] Figure 8 is a flowchart of a method for detecting the amount of iron passing through a hydraulic jaw crusher according to another embodiment of the present application.

[0037] Figure 9 is a flowchart of a method for detecting the amount of iron passing through a hydraulic jaw crusher according to another embodiment of the present application.

[0038] Figure 10 is a flowchart of a method for detecting the amount of iron passing through a hydraulic jaw crusher according to another embodiment of the present application.

[0039] Figure 11 is a flowchart of a method for detecting the amount of iron passing through a hydraulic jaw crusher according to another embodiment of the present application.

[0040] Figure 12 is a system block diagram of a system for detecting the amount of iron passing through a hydraulic jaw crusher according to an embodiment of the present application. DETAILED DESCRIPTION

[0041] The technical solutions in the present application will be clearly and completely described below with reference to the drawings in the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. The components of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application.

[0042] It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0043] When the conventional existing hydraulic jaw crusher processes complex raw materials mixed with small-size metal foreign matters, there is a technical problem of how to overcome the influence of inherent high-amplitude vibration and high-concentration dust during equipment operation. At the same time, as a consumable part, the dynamic change of the equipment response characteristics caused by the wear of the jaw plate also brings challenges to the detection of metal foreign matters. How to realize high-sensitivity and high-reliability detection of small-size metal foreign matters in such an environment, and effectively distinguish them from the signals generated by normal crushing impact of large and hard ores, is a key technical problem currently faced.

[0044] For example, it is assumed that a hydraulic jaw crusher is used for primary crushing on a high-purity quartz sand production line. The production line has very high requirements for product purity, and the downstream equipment has low tolerance to metal foreign matters. Small iron filings or small-size metal particles may be mixed in the raw materials. In this scenario, the conventional over-iron detection method based on the pressure peak of the hydraulic system or the sudden change of the motor current is not sensitive enough to effectively identify these small-size metal foreign matters. The amplitude of the hydraulic or current fluctuation caused by these small-size metal foreign matters is small, and it is easy to be overwhelmed by the signal fluctuation generated when normal crushing of large and hard ores, resulting in missed detection. In addition, as the jaw plate is continuously worn, its surface topography and mechanical response characteristics change, which may cause fluctuations in the missed detection rate or false alarm rate during the service life of the jaw plate, making it difficult to develop reliable basis for equipment condition monitoring and maintenance planning, and affecting the efficiency of production management and reliable operation of the equipment.

[0045] With reference to Figure 1 In an embodiment of the present application, a method for detecting over-iron quantity of a hydraulic jaw crusher is proposed, the method comprising:

[0046] S1: preset a probe wave, the probe wave is sent in a device-specific area and propagates;

[0047] S2: preset a plurality of wear state partitions, establish a preset baseline feature library corresponding to each wear state partition, each wear state partition corresponds to a wear state of the device;

[0048] S3: receive a response signal after the propagation of the probe wave;

[0049] S4: analyze the response signal to obtain a modulated response after the propagation of the probe wave;

[0050] S5: determine the current wear state partition, obtain the preset baseline feature library corresponding to the current wear state partition, compare the preset baseline feature library and the modulated response, and determine whether there is a metal foreign object.

[0051] In this embodiment, the preset probe wave refers to an active injection signal with specific parameters (such as frequency characteristics, excitation signal form), which can be realized by a signal generator, a piezoelectric transducer, or an electromagnetic excitation device, for example, an ultrasonic pulse is generated by a piezoelectric ceramic sheet, which is mainly used to inject controllable energy into the device structure for subsequent analysis of its propagation characteristics. The preset plurality of wear state partitions refers to dividing the device (especially the jaw plate) into several different wear stages during its service life, for example, according to the cumulative working time or the amount of processed material, reflecting the differences in mechanical response characteristics of the device under different degrees of wear.

[0052] Establishing a preset baseline feature library corresponding to each wear state partition refers to collecting and analyzing the probe wave modulation response characteristics of the device under normal working (without metal foreign objects) state for each preset wear state partition, forming a normal signal feature set under this wear state, which can be established by statistical analysis methods (such as calculating the mean, standard deviation, and fluctuation range) or pattern recognition methods, providing a reference benchmark for judging the device state under a specific wear state.

[0053] Receiving a response signal after the propagation of the probe wave refers to receiving the signal after the propagation, reflection, attenuation, and modulation of the probe wave in the device structure through a sensor (such as an acceleration sensor, an acoustic emission sensor).

[0054] Analyzing the response signal to obtain a modulated response after the propagation of the probe wave refers to processing the received response signal, extracting the signal components related to the probe wave, and analyzing its frequency characteristics or time-domain waveform features to obtain the change information reflecting the influence of the device state on the propagation process of the probe wave in the device, which can be realized by filtering, time-frequency analysis, and / or feature extraction algorithms.

[0055] The determination of the current wear state partition refers to determining which preset wear state stage the device is currently in, which can be determined according to the running parameters (such as cumulative working time, material processing amount) of the device or the monitored signal feature changes. The preset baseline feature library corresponding to the current wear state partition is obtained, and the corresponding library is selected from the established multiple preset baseline feature libraries according to the determined current wear state, which is mainly to provide a judgment basis suitable for the current device wear state.

[0056] The preset baseline feature library and the modulation response are compared to determine whether there is metal foreign matter. The real-time acquired probe test wave modulation response is compared and analyzed with the preset baseline feature library corresponding to the current wear state to determine whether the modulation response exhibits features significantly different from the normal state or matches the metal foreign matter features, which can be achieved by threshold comparison, pattern matching or machine learning algorithm, which is mainly to identify abnormal signals indicating the presence of metal foreign matter.

[0057] The core innovation of the present application is that by actively injecting a probe test wave into a specific area of the device and analyzing the modulation response after its propagation, and comparing it with the preset baseline feature library established based on different wear states of the device, the influence of the response characteristic changes caused by the wear of the jaw plate on the detection accuracy in harsh working conditions such as high vibration and high dust is effectively overcome, and high sensitivity and high reliability detection of metal foreign matter is achieved.

[0058] As a preferred embodiment, the scheme of the present embodiment is implemented as follows: an ultrasonic generator can be used as an excitation source module and installed on the non-working surface of the jaw plate for sending ultrasonic pulses of a specific frequency and waveform as a probe test wave. An ultrasonic receiving sensor is installed on the other side of the jaw plate or a specific position of the rack as a receiving sensor module for receiving the response signal of the ultrasonic pulse after its propagation in the jaw plate and surrounding structure. The system can divide the wear state of the jaw plate into four partitions according to the cumulative running time or total amount of material processed by the device: new jaw plate, slight wear, moderate wear, and severe wear. During the initial running of the device and the normal production process in different wear stages, a large number of response signals when there is no metal foreign matter are collected, the features of the ultrasonic modulation response (such as signal energy, amplitude of specific frequency component, and degree of waveform distortion) are analyzed, and the statistical range is calculated. During daily detection, the system determines the current wear state partition according to the cumulative running time, and obtains the corresponding preset baseline feature library. The features of the real-time modulation response are compared with the preset baseline feature library corresponding to the current wear state, and if the real-time features are outside the normal range, it is determined that there is metal foreign matter and an alarm is issued.

[0059] Through the above scheme, the application can effectively cope with the influence of jaw plate wear on signal response under harsh working conditions such as high vibration and high dust, improve the sensitivity and reliability of metal foreign matter detection in the hydraulic jaw crusher, effectively distinguish metal foreign matter from normal crushing impact, reduce the false positive and false negative rates, thereby reducing the damage of metal foreign matter to the equipment and ensuring the production continuity and product quality.

[0060] With reference to Figure 2 Further, the step S2 of establishing the preset baseline feature library corresponding to each wear state partition comprises the following sub-step S21:

[0061] S22: Establishing the preset baseline feature library corresponding to each wear state partition, the preset baseline feature library comprising a normal baseline feature library and a metal modulation feature library;

[0062] S23: Judging the current wear state partition, obtaining a normal baseline generated by a modulation response under normal production state, analyzing the statistical tolerance range of the normal baseline, and establishing a normal baseline feature library;

[0063] S24: Judging the current wear state partition, obtaining a first group of modulation feature signals affected by a modulation response of a mechanical disturbance device arranged on the equipment, the mechanical disturbance device applying mechanical disturbance to a specific area of the equipment to simulate the action generated by the metal contacting the jaw plate;

[0064] S25: Obtaining a second group of modulation feature signals affected by a response signal of a metal placed on the jaw plate of the equipment;

[0065] S26: Generating a metal baseline in combination with the first group of modulation feature signals and the second group of modulation signals, and establishing a metal modulation feature library corresponding to the current wear state partition.

[0066] With reference to Figure 3 The step S5 comprises the following sub-step S51 after judging the current wear state partition:

[0067] S52: Obtaining the preset baseline feature library corresponding to the current wear state partition, comparing the preset baseline feature library and the modulation response, and judging whether there is metal foreign matter;

[0068] S53: When the modulation response deviates from the statistical tolerance range and matches any metal baseline feature in the metal modulation feature library, it is judged that there is metal foreign matter.

[0069] The normal baseline feature library refers to a statistical description or range of the probe test wave modulation response of the storage device in a normal production state, which can be achieved by recording the mean, standard deviation, upper and lower threshold values, or probability distribution parameters, etc. The metal modulation feature library refers to the typical features or templates of the probe test wave modulation response generated by the storage metal foreign object passing through, which can be achieved by storing feature vectors, waveform templates, or classification model parameters, etc. The normal baseline refers to the probe test wave modulation response data samples collected when the device is in a normal production state and no metal foreign object passes through. The statistical tolerance range refers to the numerical interval or statistical range calculated based on the normal baseline data to define the boundary of normal signal fluctuations. The mechanical disturbance device refers to a device that can apply a controllable mechanical action to a specific area of the device, which can be achieved by using an electric vibrator, a pneumatic impactor, or a hydraulic actuator, etc. In this embodiment, the first group of modulation feature signals is a set of probe test wave modulation response signals collected and processed by the sensor after the disturbance is applied by the mechanical disturbance device. The second group of modulation feature signals is a set of probe test wave modulation response signals collected and processed by the sensor after the metal foreign object is actually put into the device. The metal baseline is a set or model representing the unique signal pattern or characteristic parameter of the metal foreign object extracted by analyzing and processing the first and second groups of modulation feature signals. The deviation from the statistical tolerance range refers to the fact that one or more characteristic values of the current probe test wave modulation response signal exceed the statistical tolerance range defined in the normal baseline feature library. Matching any metal baseline feature in the metal modulation feature library refers to the fact that the characteristics of the current probe test wave modulation response signal have sufficient similarity or meet the preset matching criteria with at least one metal baseline feature stored in the metal modulation feature library, which can be based on correlation analysis, distance measurement, or pattern recognition algorithm classification results, etc.

[0070] Based on the above technical features, one example of the embodiment operates according to the following principles: The system first partitions different wear states of the hydraulic jaw crusher and establishes a preset baseline feature library containing normal baseline features and metal modulation features respectively. The establishment of the normal baseline feature library is through collecting a large number of modulation response data under the normal production state of the equipment, analyzing the statistical characteristics of these data, and thus determining the fluctuation range or statistical tolerance boundary of the normal signal under the current wear state. This enables the system to identify and allow normal signal fluctuations, avoiding false positives. The establishment of the metal modulation feature library combines simulation and actual testing. The first set of modulation characteristic signals is obtained by simulating the action of metal contact with the jaw plate through a mechanical disturbance device; at the same time, the second set of modulation characteristic signals is obtained by actually putting in metal foreign matter. Combined with these two sets of signals, the metal baseline representing the characteristics of different types of metal foreign matter is generated and stored in the metal modulation feature library. In the actual detection process, the system first judges the current wear state partition of the equipment and obtains the preset baseline feature library corresponding to the partition. Then, the real-time collected modulation response is compared with the normal baseline feature library to determine whether it deviates from the normal statistical tolerance range. If the modulation response does not deviate from the normal range, it is considered that the current working condition is normal. If the modulation response deviates from the normal range, the system will further compare the features of the modulation response with the metal baseline features in the metal modulation feature library. Only when the modulation response deviates from the normal statistical tolerance range and matches any metal baseline feature in the metal modulation feature library, the system will finally judge that there is metal foreign matter. This double judgment mechanism, i.e. requiring signal abnormality (deviation from normal) and abnormal type matching metal features (matching metal library), can effectively distinguish abnormal signals caused by metal foreign matter from signals caused by non-metal foreign matter or other abnormalities of the equipment itself, significantly improving the accuracy and reliability of detection.

[0071] In order to more clearly illustrate the technical solutions of the present embodiment, a specific implementation manner is described below: a piezoelectric acceleration sensor can be installed on the non-working surface of the moving jaw or the fixed jaw of the hydraulic jaw crusher for collecting response signals when the probe test wave passes. The signal processing unit filters and envelope demodulates the collected response signals to extract the envelope signal related to the probe test wave frequency as the modulation response. The wear state partition can be divided according to the cumulative running time of the equipment or the total amount of processed materials, for example, divided into several stages such as new jaw plate, slight wear, moderate wear, severe wear, etc. In each wear state stage, a preset baseline feature library is established. The establishment of the normal baseline feature library can continuously collect the modulation response data for a period of time when the equipment normally crushes the ore, calculate the mean value and standard deviation, and add or subtract three times the standard deviation to the mean value as the normal statistical tolerance range under the wear state. The establishment of the metal modulation feature library can first use a small electromagnetic vibrator to apply a simulated impact at a specific position of the jaw plate to collect the first group of modulation characteristic signals; then, different sizes of steel balls or scrap steel sections are thrown into the crushing cavity to collect the second group of modulation characteristic signals. Time-frequency analysis is performed on the two groups of signals to extract features such as energy, peak value, duration, amplitude of specific frequency components, etc., to generate metal baseline templates representing the characteristics of different metal foreign objects and store them in the metal modulation feature library. During actual operation of the equipment, the modulation response is collected in real time. First, it is judged whether the peak value or energy exceeds the normal statistical tolerance range corresponding to the current wear state. If it exceeds, the feature vector of the current modulation response is calculated, and similarity calculation (such as correlation coefficient or Euclidean distance) is performed with all metal baseline templates in the metal modulation feature library. If the similarity is higher than a preset threshold, it is judged that there is a metal foreign object.

[0072] By adopting the above technical solutions, the accuracy and reliability of metal foreign object detection are improved, and the detection sensitivity for small size metal foreign objects is particularly high. Accurate metal foreign object detection helps to take timely measures to avoid or reduce the damage of metal foreign objects to the hydraulic jaw crusher, reduces the equipment downtime and maintenance cost, thereby improving the operation stability and production efficiency of the equipment.

[0073] Reference Figure 4 Further, S23 comprises:

[0074] S231: judging the current wear state partition, and obtaining a plurality of groups of modulation responses generated under normal production state as normal baseline;

[0075] S232: analyzing the non-steady state changes or atypical fluctuation sections of the normal baseline, obtaining the statistical tolerance range of the normal baseline under the current wear state partition according to the non-steady state changes or atypical fluctuation sections, and establishing a normal baseline feature library.

[0076] In another embodiment of the present application, the non-steady state variation or atypical fluctuation refers to the instantaneous or short-time fluctuation in the probe test wave response caused by the non-steady state variation of the feed state or the instantaneous passing of the non-metallic hard material, etc. under normal production conditions. These fluctuations are different from the normal signals in the steady state and also different from the signals caused by the metal foreign matter. The analysis of these fluctuation sections can use signal processing techniques such as time-frequency analysis, wavelet analysis or statistical analysis methods to identify their characteristics and distinguish them from the normal signals in the steady state. According to the statistical tolerance range obtained from the non-steady state variation or atypical fluctuation, the influence of these atypical fluctuations will be considered when determining the fluctuation range of the normal baseline. This can be achieved in various ways, for example, the data of these atypical fluctuation sections can be excluded when calculating the statistical tolerance, or a more robust statistical method can be used to calculate the tolerance, which will not be described here. In this way, the tolerance range can more accurately reflect the true fluctuation characteristics of the normal signals containing atypical fluctuations, and can more accurately distinguish the abnormal signals caused by metal foreign matter from the atypical fluctuations caused by normal working conditions, thereby improving the accuracy of detection.

[0077] In this embodiment, as a specific implementation, a threshold based on signal instantaneous energy or amplitude rate of change can be set, and when the energy or rate of change of the signal exceeds the threshold in a short time (for example, less than a preset duration threshold), the data of this time period is marked as an atypical fluctuation section. When calculating the statistical tolerance range of the normal baseline, various methods can be used. One method is to exclude all data marked as atypical fluctuation sections and only use the remaining stable data to calculate the mean and standard deviation, and then determine the tolerance range according to the preset confidence level. Another method is to perform statistical analysis on all data (including atypical fluctuation sections), but use robust statistical quantities that are not sensitive to outliers, such as calculating the median and interquartile range of the data, and determining the tolerance range based on these statistical quantities. In this way, the statistical tolerance range obtained can more accurately reflect the fluctuation characteristics of the normal baseline under the current wear state, including the influence of atypical fluctuations.

[0078] Further, the non-steady state variation of the feed state or the instantaneous passing of the non-metallic hard material, etc. will also cause atypical fluctuations of the probe test wave, and these atypical fluctuations may have similarities in characteristics with the fluctuations caused by metal foreign matter, which can easily lead to misjudgment and affect the accuracy of metal foreign matter detection if not distinguished.

[0079] Reference Figure 5 Further optimization, S232 includes:

[0080] S2321: preset atypical fluctuation discrimination basis corresponding to each wear state partition respectively;

[0081] S2322: According to the current wear state partition, select the corresponding non-typical fluctuation discrimination basis, and analyze the normal baseline according to the corresponding non-typical fluctuation discrimination basis to identify the non-steady state change or non-typical fluctuation segment of the normal baseline. The non-typical fluctuation discrimination basis is used to define the non-typical fluctuation of the probe test wave caused by the non-steady state change of the feed state or the instantaneous passing of the non-metallic hard material under the corresponding wear state.

[0082] The non-typical fluctuation discrimination basis is used to distinguish the fluctuation of the probe test wave caused by the non-metallic factor (such as the change of the feed state or the passing of the non-metallic hard material) from the fluctuation of the normal baseline. The rules or models can be achieved by using statistical characteristics (such as specific thresholds or ranges of waveform amplitude, duration, and frequency components), machine learning models (such as classifiers), or expert experience rules.

[0083] Because the response characteristics of the equipment to the change of the feed state or the passing of the non-metallic hard material may be different under different wear states, the corresponding non-typical fluctuation discrimination basis is set for each wear state partition in advance. The system selects the corresponding non-typical fluctuation discrimination basis according to the current wear state partition. Then, the system uses the selected discrimination basis to analyze the normal baseline formed by the probe test wave response signal collected under the normal production state. By applying the discrimination basis, the system can identify the non-steady state change or non-typical fluctuation segment of the normal baseline caused by the non-metallic factor. These identified non-typical fluctuation segments will be excluded or specially processed in order to more accurately calculate the statistical tolerance range of the remaining normal baseline. Finally, based on the normal baseline data after excluding the non-typical fluctuation interference, the normal baseline feature library under the current wear state partition is established or updated. This process ensures the accuracy of the normal baseline feature library because it excludes the false fluctuations caused by non-metallic interference, making the subsequent metal foreign object judgment more reliable.

[0084] In one implementation of the embodiment, the wear state of the device can be divided into three zones: early, middle and late. For the early wear zone, the preset atypical fluctuation criterion can be a rule based on amplitude and duration, for example, when the fluctuation amplitude of the normal baseline exceeds a certain threshold A, but the duration is less than a certain threshold B, it is determined as an atypical fluctuation. For the middle wear zone, due to the wear of the jaw plate, the signal characteristics may change, so the criterion can be adjusted to thresholds A' and B' (A'>A, B' can be different from B), or the frequency characteristics can be introduced as the criterion. For the late wear zone, a pre-trained classification model can be used as the criterion, which can identify specific waveform patterns caused by non-metallic hard materials passing through in the late wear state. In actual application, the system first determines which wear zone the current device is in, for example, by monitoring the cumulative working time of the device to determine that it is in the middle wear zone. Then, the system selects the criterion corresponding to the middle wear zone. When analyzing the normal baseline data, the system applies the criterion of the middle wear zone to identify the non-steady state changes or atypical fluctuation segments. For example, for a fluctuation in the baseline, if its amplitude exceeds the threshold A' or its frequency component meets the frequency characteristic range corresponding to the middle wear zone, it will be marked as an atypical fluctuation segment. When calculating the statistical tolerance range of the normal baseline, the data of these marked atypical fluctuation segments are excluded, and only the remaining normal baseline data is used for statistical calculation, so as to obtain a more accurate statistical tolerance range, and based on this, a normal baseline feature library is established.

[0085] Referring to Figure 6 Further, in one embodiment of the present application, the method of determining the current wear state zone comprises:

[0086] S501: receiving the cumulative working time of the device and / or the quantity of processed materials;

[0087] S502: determining the current wear state zone by a preset judgment rule.

[0088] The cumulative working time of the device is the total running time of the device since it started running, which can be obtained by using a timer, system log recording, etc. The quantity of processed materials is the total amount of materials processed by the device during operation, which can be obtained by using a material flow meter, production batch record, or estimated based on working time and average processing rate. The preset judgment rule refers to the logic or standard set before the system is put into use or during operation for mapping the cumulative working time of the device or the quantity of processed materials to a specific wear state zone, which can take the form of threshold table, piecewise function, lookup table, etc. The wear state zone refers to different stages divided according to the wear degree of the key components of the device, which can be divided into multiple zones, and these zones correspond to different wear degrees.

[0089] The way of selecting a corresponding baseline for comparison based on the current actual wear state can effectively compensate the influence of the jaw plate wear on the signal response characteristics, so that the detection algorithm can more accurately distinguish normal signal fluctuations and abnormal signals caused by metal foreign matter. By automatically determining the wear state partition, the lag and inaccuracy of manual judgment are avoided, and the stable performance of the detection system in the entire service life of the equipment is ensured.

[0090] For example, three wear state partitions can be set: partition 1 corresponds to the new jaw plate stage, partition 2 corresponds to the light wear stage, and partition 3 corresponds to the moderate wear stage. The preset judgment rule can be set as: if the cumulative working time of the equipment is less than or equal to 1000 hours, it is judged as partition 1; if the cumulative working time of the equipment is greater than 1000 hours and less than or equal to 3000 hours, it is judged as partition 2; if the cumulative working time of the equipment is greater than 3000 hours, it is judged as partition 3. Alternatively, the judgment rule can be based on the quantity of processed materials. In specific implementation, a timer or a material counter can be used to monitor the cumulative working time or the quantity of processed materials of the equipment in real time, and send the data to a processing unit. The processing unit internally stores the above-mentioned judgment rule, executes the judgment logic according to the received data, and outputs the current wear state partition information.

[0091] By receiving the cumulative working time and / or the quantity of processed materials of the equipment and using the preset judgment rule, the application realizes automatic and real-time judgment of the wear state partition, improves the judgment efficiency and real-time performance, and does not require manual intervention, which can better adapt to the needs of the continuous production of the crusher.

[0092] Reference Figure 7 Further, another embodiment of the application provides a method for judging the current wear state partition, comprising:

[0093] S503: monitoring the transition parameter drift of the modulation response in the transition process between two adjacent wear state partitions in the normal production state, establishing a wear interval baseline library, and the wear interval baseline library contains the baseline fluctuation range corresponding to any wear state partition;

[0094] S504: when the fluctuation of the modulation response deviates from the baseline fluctuation range corresponding to the current wear state partition in the normal production state, it is judged that the next wear state partition is entered.

[0095] wherein the transition parameter drift refers to the trend or shift in certain key parameters of the modulated response obtained after the propagation of the probe wave during the gradual transition of the device from one wear state partition to the next. This drift can be quantified and tracked by continuously monitoring the changes in the statistical properties of the modulated response over time or cumulative workloads. The wear interval baseline library is a collection of data that stores the normal fluctuation range of the modulated response under different wear state partitions. This baseline library can be a lookup table, a database, or a set of parametric models, where each entry or model corresponds to a wear state partition and records the typical fluctuation range of the modulated response under that partition. The baseline fluctuation range refers to the expected variation interval of the probe wave modulated response when the device is in normal production state under a specific wear state partition. This range can be determined by collecting a large amount of normal production data when the device is in a specific wear state partition and no metal foreign objects pass through, and calculating the statistical distribution of the modulated response.

[0096] The embodiment monitors the transition parameter drift of the modulation response in the transition process between two adjacent wear state partitions in the normal production state, masters the response characteristics of the equipment in the transition stage of different wear states, and these characteristics can more accurately reflect the change of the wear state. Based on these monitoring data, a wear interval baseline library is established, which contains the baseline fluctuation range corresponding to any wear state partition. This baseline fluctuation range defines the normal fluctuation range of the modulation response under a specific wear state. When the equipment is in normal production, the fluctuation of the modulation response is continuously monitored and compared with the baseline fluctuation range corresponding to the current judged wear state partition. When the fluctuation of the modulation response deviates from the baseline fluctuation range corresponding to the current wear state partition, this deviation is considered as a sign of change of the wear state, and the system judges that the equipment enters the next wear state partition. For example, in the initial stage of equipment operation, multiple wear state partitions can be defined, such as "new jaw plate", "light wear", "moderate wear" and "heavy wear". During the process of gradually wearing from "new jaw plate" to "light wear", some parameters of the probe test wave modulation response are continuously monitored, such as the amplitude or energy of a specific frequency. The trend of change of these parameters with the cumulative amount of processed material or working time, i.e. the transition parameter drift, is recorded. Based on these monitoring data, a wear interval baseline library is established. The baseline fluctuation range corresponding to each wear state partition is stored in the baseline library. For example, the baseline fluctuation range corresponding to the "new jaw plate" partition is that parameter A fluctuates in the interval [A1, A2], and the baseline fluctuation range corresponding to the "light wear" partition is that parameter A fluctuates in the interval [A3, A4], where [A3, A4] can be different from [A1, A2]. During normal production of the equipment, the system continuously monitors the real-time fluctuation of parameter A. If the equipment is currently judged to be in the "new jaw plate" partition, the system checks whether the fluctuation of parameter A is within the range [A1, A2]. When the fluctuation of parameter A begins to continuously exceed the range [A1, A2] and may enter or approach the range [A3, A4], the system judges that the equipment has entered the "light wear" partition. Once it is judged to enter the "light wear" partition, the system will load the preset "light wear" partition corresponding to the atypical fluctuation criterion, and use this criterion to analyze the normal baseline, establish the normal baseline characteristic library under the "light wear" partition, and use it for subsequent metal foreign body detection.

[0097] The present application can dynamically judge the current wear state partition based on the characteristic change of the equipment response signal itself. This method overcomes the misjudgment problem caused by relying only on preset rules, and improves the accuracy of wear state partition judgment. Accurate wear state partition judgment enables the subsequent normal baseline analysis and metal foreign body detection to use parameters and criteria that are more matched with the current equipment state, thereby improving the accuracy and reliability of metal foreign body detection throughout the life cycle of the equipment, and reducing false negatives and false positives.

[0098] Referring to Figure 8 Further, S2322 comprises:

[0099] S23221: monitoring at least one operating parameter of the equipment, the operating parameter being related to the material currently processed by the equipment or the working condition of the non-jaw plate component;

[0100] S23222: determining the current material type or the working condition of the non-jaw plate component according to the operating parameter;

[0101] S23223: monitoring the normal production, and if the fluctuation of the response signal after the change of the operating parameter is not typical fluctuation, taking it as the basis for distinguishing the atypical fluctuation;

[0102] Referring to Figure 9 Step S504 further comprises:

[0103] S5041: obtaining the corresponding basis for distinguishing the atypical fluctuation according to the current operating parameter and the wear state partition;

[0104] S5042: judging whether the fluctuation of the current response signal meets the current basis for distinguishing the atypical fluctuation;

[0105] S5043: if the fluctuation of the current response signal meets the current basis for distinguishing the atypical fluctuation, judging to keep the current wear state partition; if the fluctuation of the current response signal does not meet the current basis for distinguishing the atypical fluctuation, and the fluctuation of the current response signal deviates from the baseline fluctuation range corresponding to the current wear state partition, judging to enter the next wear state partition.

[0106] Wherein, the operating parameter represents a physical quantity or a state quantity reflecting the current working state of the equipment or external input conditions, the change of which is related to the material characteristics processed by the equipment or the working state of the non-jaw plate component. The operating parameter is realized by using signals collected by sensors such as current, voltage, hydraulic system pressure, motor speed, feeding speed, discharge size and / or bearing temperature. The material type represents the kind of material currently processed by the equipment determined according to the monitored operating parameter, and the working condition of the non-jaw plate component is the working state of the non-jaw plate component. The judgment is realized by using preset parameter threshold, parameter change rate analysis, or pattern recognition algorithm based on historical data. The baseline fluctuation range represents the statistical tolerance range of the fluctuation of the response signal under normal production conditions in a specific wear state partition. The range is defined as the signal fluctuation boundary when there is no metal foreign matter passing through and the working condition is relatively stable under the current wear state. The satisfaction of the current basis for distinguishing the atypical fluctuation means that the fluctuation characteristics of the real-time monitored response signal are consistent with the characteristics defined by the basis for distinguishing the atypical fluctuation corresponding to the current operating parameter and wear state partition. Whether it is satisfied is realized by using feature matching algorithm, threshold comparison, or classifier judgment.

[0107] The embodiment further refines the judgment logic on the basis of the existing method of judging wear state partition switching by monitoring the deviation of response signal fluctuation from the baseline range. First, the system continuously monitors at least one operating parameter of the equipment, which is related to the characteristics of the material currently processed by the equipment or the working state of the non-jaw plate components. By analyzing these operating parameters, the system determines the current specific working condition, such as whether it is processing hard material or soft material, or whether there is an abnormality in a certain non-jaw plate component. At the same time, the system pre-establishes non-typical fluctuation criteria for different working conditions and wear state combinations by monitoring the fluctuation characteristics of the response signal after the operating parameters change under normal production conditions in different wear state partitions. These criteria record the signal fluctuation patterns caused by non-metal factors under specific working conditions and wear stages. When the fluctuation of the response signal deviates from the baseline fluctuation range corresponding to the current wear state partition, it is no longer directly judged to enter the subsequent wear state partition. Instead, it is first determined according to the current operating parameters and the established wear state partition to find and obtain the corresponding non-typical fluctuation criteria. Then, the current response signal fluctuation characteristics are compared with the obtained non-typical fluctuation criteria. If the current response signal fluctuation meets the non-typical fluctuation criteria, it is considered that the current fluctuation is a non-typical fluctuation caused by non-metal factors, rather than a change, and the current wear state partition is maintained. Only when the current response signal fluctuation neither meets the current non-typical fluctuation criteria nor deviates from the baseline fluctuation range corresponding to the current wear state partition, it is finally judged to enter the subsequent wear state partition. This effectively distinguishes between baseline drift caused by wear and instantaneous fluctuation caused by working condition changes or non-metal foreign objects, thereby avoiding misjudgment or delayed judgment of wear state partition caused by non-typical fluctuation. By accurately judging the wear state partition, the system always applies detection parameters and pre-set baseline feature libraries that match the current wear degree, thereby maintaining high metal foreign object detection accuracy and reliability throughout the service life.

[0108] In one specific embodiment, monitoring the operating parameters of the device includes monitoring pressure signals of the hydraulic system and current signals of the drive motor. These signal changes reflect the hardness of the processed material, the uniformity of the feed material, and the load conditions of the transmission system. For example, a sudden increase in hydraulic pressure and motor current indicates a sudden increase in the amount of hard material being processed. The system determines the current material type or non-jaw component working condition according to these combined changes. For example, hard rock and soft rock are determined by the average value peak of pressure and current, and transmission abnormalities are determined by periodic fluctuations in current. In establishing the basis for distinguishing atypical fluctuations, during normal production of the device, human records of naturally occurring working condition changes are recorded, such as adjusting the feed speed to process different batches of material, while recording the hydraulic pressure, motor current, and response signal fluctuation characteristics under different wear state partitions. For example, when the amount of feed is suddenly increased, the amplitude fluctuation range and duration of the response signal are recorded under light wear state. These recorded fluctuation characteristics, combined with the current operating parameter wear state partition, form part of the basis for distinguishing atypical fluctuations. These bases are stored in a lookup table database. In actual operation, when the monitoring deviates from the baseline fluctuation range corresponding to the current wear state partition, the system first reads the current hydraulic pressure and motor current, and combines the current wear state partition information to obtain the atypical fluctuation distinguishing basis that matches the current working condition and wear state from the pre-set lookup table. For example, if the current wear state is moderate, the hydraulic pressure and motor current indicate that hard rock is being processed, and the system obtains the atypical fluctuation distinguishing basis for the moderate wear state under hard rock working conditions. Then, the system compares the current response signal fluctuation characteristics, such as fluctuation amplitude, frequency component, and duration, with the atypical fluctuation distinguishing basis. If the current fluctuation characteristics, such as amplitude within the range and duration less than the threshold, match the recorded characteristics in the atypical fluctuation distinguishing basis, it is determined that the fluctuation is caused by non-metallic factors, and the current wear state partition is maintained. Only when the current fluctuation characteristics do not match the atypical fluctuation distinguishing basis and still deviate from the baseline fluctuation range, it is determined to enter the subsequent wear state partition, avoiding misjudgment of atypical fluctuations as wear state changes and metal foreign objects when the working condition changes, and reducing false positives. At the same time, when determining the wear state partition switching, by excluding the influence of fluctuations that meet the atypical fluctuation characteristics, the partition determination is stable and accurate, avoiding premature or delayed switching of the partition due to transient working condition disturbances. This ensures that the system applies appropriate detection parameter baselines throughout the service life, improving the overall reliability and adaptability of metal foreign object detection.

[0109] Reference Figure 10 Further, the test wave setting method of the embodiment is tested as follows:

[0110] S100: Determine the pre-set parameters of the test wave, including the frequency characteristics and the shape characteristics of the excitation signal;

[0111] S101: Select the frequency characteristics based on the structure, material, and environmental noise characteristics of the device when it is working;

[0112] S102: Set the form feature of the probe test wave based on the selected frequency characteristic.

[0113] The preset parameters of the probe test wave refer to a set of setting values used to define the physical properties of the probe test wave, which determine the way the probe test wave propagates in the device and interacts with the material. The preset parameters include the frequency characteristic and the form feature of the excitation signal. The frequency characteristic refers to the energy distribution or response characteristics of the probe test wave at different frequencies, which can be represented by a single frequency, a combination of multiple discrete frequencies, or a continuous frequency range. The form feature of the excitation signal refers to the waveform characteristics of the probe test wave on the time axis, which can be realized by pulse signals, continuous wave signals, or complex waveform signals modulated in amplitude, frequency or phase. The structure of the device refers to the physical structure of the jaw crusher, such as the shape of the jaw plate, the geometric size of the crushing chamber, the connection method of the frame, etc. The material of the device refers to the material properties of the main components of the jaw crusher, such as high manganese steel for the jaw plate and structural steel for the frame. These structural and material characteristics will affect the propagation path, attenuation degree, and reflection and scattering mode of the probe test wave in the device.

[0114] The environmental noise characteristics during operation refer to the properties of various interference signals generated by the jaw crusher under normal working conditions, such as mechanical vibration, acoustic wave or electromagnetic interference caused by crushing materials, equipment vibration, hydraulic system action, etc. These noises have specific frequency distribution and amplitude characteristics, which may interfere with the reception and analysis of the probe test wave.

[0115] In one specific embodiment, the method is applied to a hydraulic jaw crusher for metal foreign object detection, the crusher has a high manganese steel material for the jaw plate and a welded steel structure for the frame, and there are strong mechanical vibrations and crushing noises during operation. In order to determine the preset parameters of the probe test wave, the structure and material properties of the device are first analyzed, such as through finite element simulation or experimental measurement, to understand the propagation and attenuation of signals of different frequencies in the structure. At the same time, the environmental noise of the crusher under typical working conditions is analyzed, and the main noise frequency range is determined. Based on these analysis results, a frequency characteristic with a center frequency in the ultrasonic range is selected, which has a low attenuation in the high manganese steel jaw plate and a large difference in acoustic impedance with metal materials, which is conducive to generating obvious reflection signals, and at the same time, the frequency range avoids the main mechanical vibration and crushing noise frequency. Based on the selected ultrasonic frequency characteristic, the shape feature of the probe test wave is set as a short pulse signal, such as an ultrasonic pulse with a duration of tens of microseconds, which has a high peak energy and can stimulate the device response in a short time, and its time domain characteristics are conducive to distinguishing between direct waves and reflected waves. The preset ultrasonic pulse signal is sent to the jaw plate through the excitation source for propagation, and the receiving sensor receives the response signal after propagation for subsequent analysis. This optimized probe test wave can more effectively penetrate the material and enhance the interaction signal with the metal foreign object, while significantly reducing the interference of the device structure, material and environmental noise on signal propagation and reception.

[0116] Reference Figure 11 In another embodiment of the present application, step S4 comprises:

[0117] S41: Obtain at least one working condition parameter related to the current processing material type, particle size distribution and / or water content of the device;

[0118] S42: Adjust the method or parameter of adjusting the frequency characteristic or time domain waveform feature of the signal component related to the probe test wave in the analysis response signal according to at least one working condition parameter;

[0119] S43: Use the adjusted method or parameter to analyze the signal component related to the probe test wave in the response signal to obtain the frequency characteristic or time domain waveform feature, and the method or parameter includes the algorithm for extracting the frequency characteristic or time domain waveform feature of the signal component, the filter parameter, the threshold setting and / or the feature calculation window;

[0120] S44: Analyze the frequency characteristic or time domain waveform feature of the response signal, compare and analyze the frequency characteristic or time domain waveform feature with the modulation feature and / or disturbance feature in the preset baseline feature library under the current wear state partition, separate the signal component of the modulation information related to the probe test wave, and take the signal component as the modulation response.

[0121] At least one operating parameter refers to one or more measurable or obtainable values or identifiers that describe the characteristics of the material being processed under the current operating condition of the device, which can be achieved by direct measurement with sensors, operator input, or indirectly obtained by analyzing other device operating data. Frequency characteristics refer to the energy distribution or response of the signal at different frequencies, which can be described using Fourier transform, wavelet analysis, etc. Time-domain waveform features refer to the shape, amplitude, duration, etc. of the signal over time, which can be described using peak value, root mean square value, waveform envelope, amplitude at specific time points, etc. Methods or parameters refer to specific technical means or their configuration settings for processing and analyzing the response signal, which can be achieved by using different signal processing algorithms, filter types and their coefficients, thresholds for judgment or classification, and time or frequency ranges for intercepting or selecting signal segments for analysis. Algorithm refers to a mathematical process or a set of computing steps that perform a specific computing or data processing task, which can be achieved by using digital signal processing algorithms, pattern recognition algorithms, machine learning algorithms, etc. Filter parameters refer to the values that configure the signal filter to achieve a specific filtering effect, which can be achieved by using cutoff frequency, bandwidth, filter order, filter type (such as Butterworth, Chebyshev, etc.). Threshold setting refers to the preset numerical limit for comparison, judgment or decision, which can be achieved by using amplitude threshold, time threshold, frequency threshold, correlation coefficient threshold, etc. Feature calculation window refers to the range in the time domain or frequency domain of the signal that is used to calculate specific features, which can be achieved by using a fixed time length sliding window, a time window triggered based on signal events, or a frequency band window of a specific frequency range.

[0122] The modulation features and / or disturbance features in the preset baseline feature library refer to the collection of signal features representing the normal signal fluctuation range or specific non-metallic disturbance (such as large hard material passing through) caused by pre-acquisition and analysis of a large number of response signals when the device is in normal non-metallic foreign object passing and in a specific wear state partition, and the collection of signal features representing the metal foreign object caused by acquisition and analysis when there is a metal foreign object, which can be stored in the form of feature vectors, feature templates, statistical distribution models, etc. The signal component of the modulation information refers to the signal part of the response signal that carries the information of the existence of the metal foreign object, which is generated by the interaction of the probe wave with the specific region (e.g. jaw plate) of the device and affected by the metal foreign object, which can be extracted from the original response signal by using signal decomposition, filtering, correlation analysis, etc.

[0123] The present embodiment understands the possible influence of the current crushing environment on the probe test wave response signal by acquiring working condition parameters related to the material currently being processed by the device, such as material type, particle size distribution, or moisture content. Based on these working condition parameters, the system can dynamically adjust the methods or parameters used to analyze the signal components related to the probe test wave in the response signal. This adjustment can include selecting different signal processing algorithms, modifying filter parameters to more effectively filter out noise or interference under certain working conditions, adjusting threshold settings to accommodate different signal amplitude ranges, or changing the feature calculation window to better capture signal transient changes under certain material characteristics. By using these methods or parameters adjusted according to the actual working conditions, the system can more accurately extract the signal components related to the probe test wave from the complex response signal and obtain their frequency characteristics or time-domain waveform features.

[0124] In an embodiment of the present application, sensors can be provided to measure the moisture content and average particle size of the material entering the crushing chamber, and these measurements are used as working condition parameters. The system can pre-store a parameter lookup table that corresponds different signal filter parameters (e.g., a lower cutoff frequency to filter out low-frequency noise caused by water for high moisture content) and feature calculation window length (e.g., a slightly longer window is used for larger particle size to capture the complete impact response) according to different moisture content and average particle size ranges. When the current moisture content and average particle size are acquired, the system looks up the corresponding filter parameters and window length. Then, the received response signal is filtered using these parameters to configure a digital filter, and the energy or amplitude of a specific frequency component of the signal is calculated as a time-domain or frequency characteristic within the set window. These characteristics are then compared with the pre-set baseline feature library under the current wear state partition to determine whether there are modulation features that match metal foreign objects.

[0125] Reference Figure 12 Further, the present application also provides a hydraulic jaw crusher over-iron detection system, which is used for detecting metal foreign objects in a hydraulic jaw crusher, and the system comprises:

[0126] An excitation source module is configured to generate a probe test wave to a specific area of the device;

[0127] A receiving sensor module is configured to receive a response signal after the propagation of the probe test wave;

[0128] A plurality of pre-set wear state partitions are established, and a pre-set baseline feature library corresponding to each wear state partition is established, and each wear state partition corresponds to a wear state of the device;

[0129] An analysis module is configured to analyze the response signal and obtain a modulated response after the propagation of the probe test wave;

[0130] The judging module is used for judging the current wear state partition, obtaining a preset baseline feature library corresponding to the current wear state partition, comparing the preset baseline feature library and the modulation response, and judging whether the metal foreign matter exists.

[0131] The excitation source module is a device for generating and emitting the probe test wave to the specific area of the equipment, which can be realized by a piezoelectric transducer, an electromagnetic exciter or an ultrasonic transmitter. The receiving sensor module is a device for receiving the response signal generated after the propagation of the probe test wave, which can be realized by an acceleration sensor, an acoustic emission sensor or a strain sensor. The analysis module is a unit for processing the received response signal and extracting the modulation information related to the probe test wave, which can be realized by a digital signal processor (DSP), a microcontroller or a general-purpose computer. The judging module is used for comparing the current equipment state and the modulation information extracted by the analysis module with the preset baseline feature library, so as to determine whether the metal foreign matter exists, which can be realized by a microprocessor, a programmable logic controller (PLC) or an application specific integrated circuit (ASIC). Through the cooperation of the modules, the automatic detection of the metal foreign matter in the hydraulic jaw crusher is realized.

[0132] The above only describes the embodiments of the present application and is not used for limiting the protection scope of the present application. For those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of detecting excess iron in a hydraulic jaw crusher, characterized in that, The method comprises: presetting a probe test wave, the probe test wave being transmitted in a jaw plate region of a device and propagating; presetting a plurality of wear state partitions, establishing a preset baseline feature library corresponding to each wear state partition, each wear state partition corresponding to a wear state of the device; receiving a response signal after the propagation of the probe test wave; analyzing the response signal to obtain a modulation response after the propagation of the probe test wave; judging the current wear state partition, obtaining the preset baseline feature library corresponding to the current wear state partition, comparing the preset baseline feature library and the modulation response, and judging whether there is a metal foreign object. The establishment of the preset baseline feature library corresponding to each wear state partition comprises: establishing a preset baseline feature library corresponding to each wear state partition, the preset baseline feature library comprising a normal baseline feature library and a metal modulation feature library; judging the current wear state partition, obtaining a normal baseline generated by a plurality of sets of modulation responses in a normal production state, analyzing a statistical tolerance range of the normal baseline, and establishing the normal baseline feature library; judging the current wear state partition, obtaining a first set of modulation characteristic signals from the modulation response affected by a mechanical disturbance device arranged on the device, the mechanical disturbance device applying mechanical disturbance to the jaw plate region of the device to simulate the effect of metal contact with the jaw plate; obtaining a second set of modulation characteristic signals from the response signal affected by the metal placed on the jaw plate of the device; generating a metal baseline by combining the first set of modulation characteristic signals and the second set of modulation characteristic signals, and establishing the metal modulation feature library corresponding to the current wear state partition; The obtaining of the preset baseline feature library corresponding to the current wear state partition, the comparison of the preset baseline feature library and the modulation response, and the judgment of whether there is a metal foreign object comprise: when the modulation response deviates from the statistical tolerance range and matches any metal baseline feature in the metal modulation feature library, it is judged that there is a metal foreign object.

2. The method of claim 1, wherein the method is characterized by: The judgment of the current wear state partition, the obtaining of a normal baseline generated by a plurality of sets of modulation responses in a normal production state, the analysis of a statistical tolerance range of the normal baseline, and the establishment of the normal baseline feature library comprise: judging the current wear state partition, obtaining the normal baseline generated by a plurality of sets of modulation responses in a normal production state; analyzing a non-steady state change or a non-typical fluctuation section of the normal baseline, obtaining the statistical tolerance range of the normal baseline under the current wear state partition according to the non-steady state change or the non-typical fluctuation section, and establishing the normal baseline feature library.

3. The method of claim 2, wherein the method further comprises: The analysis of the non-steady state change or the non-typical fluctuation section of the normal baseline, the obtaining of the statistical tolerance range of the normal baseline under the current wear state partition according to the non-steady state change or the non-typical fluctuation section, and the establishment of the normal baseline feature library comprise: presetting a non-typical fluctuation discrimination basis corresponding to each wear state partition respectively; According to the current wear state partition, corresponding non-typical fluctuation discrimination basis is selected, and the normal baseline is analyzed according to the corresponding non-typical fluctuation discrimination basis, so as to identify the non-steady state change or non-typical fluctuation section of the normal baseline. The non-typical fluctuation discrimination basis is used to define the non-typical fluctuation of the test wave caused by the non-steady state change of the feeding state or the instantaneous passing of the non-metallic hard material under the corresponding wear state.

4. The method of claim 1, wherein the method further comprises: The judgment of the current wear state partition includes: Receiving the cumulative working time of the equipment and / or the quantity of the processed material, and judging the current wear state partition through a preset judgment rule.

5. The method of claim 3, wherein the method is characterized by: The judgment of the current wear state partition includes: Monitoring the transition parameter drift of the modulation response in the transition process of two adjacent wear state partitions under normal production conditions, establishing a wear interval baseline library, and the wear interval baseline library contains the baseline fluctuation range corresponding to any wear state partition; When the fluctuation of the modulation response deviates from the baseline fluctuation range corresponding to the current wear state partition under normal production conditions, it is judged to enter the next wear state partition.

6. The method of claim 5, wherein the method further comprises: The judgment of the current wear state partition includes: Monitoring at least one operating parameter of the equipment, which is related to the material currently processed by the equipment or the working condition of the non-jaw plate component; Judging the current material type or the working condition of the non-jaw plate component according to the operating parameter; Monitoring the fluctuation of the response signal after the change of the operating parameter under normal production conditions, which is the non-typical fluctuation discrimination basis of several wear state partitions; When the fluctuation of the response signal deviates from the baseline fluctuation range corresponding to the current wear state partition under normal production conditions, it is judged to enter the next wear state partition. According to the current operating parameter and the wear state partition, the corresponding non-typical fluctuation discrimination basis is obtained; Judging whether the current fluctuation of the response signal meets the current non-typical fluctuation discrimination basis; If the current fluctuation of the response signal meets the current non-typical fluctuation discrimination basis, it is judged to remain in the current wear state partition; if the current fluctuation of the response signal does not meet the current non-typical fluctuation discrimination basis, and the current fluctuation of the response signal deviates from the baseline fluctuation range corresponding to the current wear state partition, it is judged to enter the next wear state partition.

7. The method of claim 1, wherein the method further comprises: The preset test wave is sent to the jaw plate area of the equipment and propagates, including: Determining the preset parameters of the test wave, including the frequency characteristics and the shape characteristics of the excitation signal; Selecting the frequency characteristics based on the structure, material and environmental noise characteristics of the equipment when it is working; Based on the selected frequency characteristics, the shape characteristics of the test wave are set.

8. The method of claim 7, wherein the method further comprises: The analysis of the response signal obtains the modulation response after the propagation of the probe test wave, including: Obtaining at least one working condition parameter related to the current processing material type, particle size distribution and / or moisture content of the device; According to the at least one working condition parameter, adjusting the method or parameter of analyzing the frequency characteristics or time domain waveform features of the signal component related to the probe test wave in the response signal; Using the adjusted method or parameter, analyzing the signal component related to the probe test wave in the response signal to obtain the frequency characteristics or time domain waveform features, the method or parameter including algorithms for extracting frequency characteristics or time domain waveform features of signal components, filter parameters, threshold settings and / or feature calculation windows; Analyzing the frequency characteristics or time domain waveform features of the response signal, comparing the frequency characteristics or time domain waveform features with the preset baseline feature library under the current wear state partition, separating the signal component of the modulation information related to the probe test wave, and taking the signal component as the modulation response.

9. A hydraulic jaw crusher over-iron detection system for detecting metal foreign objects in a hydraulic jaw crusher, characterised in that, The system comprises: An excitation source module for sending a probe test wave to the jaw plate area of the device; A receiving sensor module for receiving the response signal after the propagation of the probe test wave; A plurality of preset wear state partitions, each of which corresponds to a preset baseline feature library, and each of the wear state partitions corresponds to a wear state of the device; An analysis module for analyzing the response signal and obtaining the modulation response after the propagation of the probe test wave; A judgment module for judging the current wear state partition, obtaining the preset baseline feature library corresponding to the current wear state partition, comparing the preset baseline feature library and the modulation response, and judging whether there is metal foreign matter; The establishment of the preset baseline feature library corresponding to each wear state partition includes: Establishing a preset baseline feature library corresponding to each wear state partition, the preset baseline feature library including a normal baseline feature library and a metal modulation feature library; Judging the current wear state partition, obtaining the normal baseline generated by a plurality of groups of normal production states, analyzing the statistical tolerance range of the normal baseline, and establishing the normal baseline feature library; Judging the current wear state partition, obtaining the first group of modulation feature signals affected by the modulation response of the mechanical disturbance device arranged on the device, the mechanical disturbance device applying mechanical disturbance to the jaw plate area of the device to simulate the effect of metal contact with the jaw plate of the device; Obtaining the second group of modulation feature signals affected by the response signal of the metal placed on the jaw plate of the device; Combining the first group of modulation feature signals and the second group of modulation feature signals to generate a metal baseline, and establishing the metal modulation feature library corresponding to the current wear state partition; The obtaining of the preset baseline feature library corresponding to the current wear state partition, the comparison of the preset baseline feature library and the modulation response, and the judgment of whether there is metal foreign matter include: When the modulation response is detected to deviate from the statistical tolerance range, and matches any of the metal baseline features in the metal modulation feature library, it is judged that there is a metal foreign matter.

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