A printing device predictive maintenance method based on big data analysis

Abstract

The present application relates to the technical field of intelligent maintenance of printing equipment, and provides a printing equipment predictive maintenance method based on big data analysis, which comprises the following steps: acquiring paper feeding state data, production process data and environmental data; determining effective micro-delay amount and delay occurrence position based on the difference between the theoretical arrival time and the actual arrival time of paper in the paper feeding path, and making a preliminary judgment on the printing equipment according to the above data to identify normal paper feeding state, abnormal adsorption paper feeding state, abnormal mechanical handover state and uncertain paper feeding state; when being in the uncertain paper feeding state, making a secondary judgment by further combining paper type, paper storage time length and workshop humidity to identify paper moisture absorption abnormal state, dust pollution abnormal state and working condition adaptation abnormal state, and formulating corresponding maintenance strategies. The present application can identify paper feeding abnormalities in advance, improve abnormal identification accuracy and maintenance pertinence, and reduce downtime and maintenance cost.

Description

Technical Field

[0001] This invention relates to the field of intelligent maintenance technology for printing equipment, and more specifically to a predictive maintenance method for printing equipment based on big data analysis. Background Technology

[0002] In the operation of offset printing presses, packaging printing presses, and high-speed sheet-fed printing equipment, the paper feeding system is a crucial component affecting printing stability. Problems such as unstable paper feeding, pre-positioning deviations, abnormal paper handover, or unstable paper roller conveying can easily lead to paper jams, registration misalignment, increased scrap rates, and even equipment shutdowns, thus impacting printing production efficiency and product quality.

[0003] Most existing printing equipment maintenance methods rely on post-failure repair or periodic maintenance, which typically depend on manual experience to judge the equipment's condition. For example, operators only inspect and adjust the feeder mechanism, paper gripper mechanism, or paper feed rollers after a significant paper jam, paper feeding stoppage, or printing misalignment occurs. This method makes it difficult to promptly identify minor abnormal changes that occur early in the paper feeding process, resulting in equipment malfunctions often being discovered only after they have escalated, leading to a significant maintenance lag.

[0004] Furthermore, existing technologies for anomaly detection mostly focus only on the mechanical structure itself, lacking comprehensive analysis of process and environmental factors such as paper type, paper storage time, and workshop humidity. When paper absorbs moisture, dust spreads, or equipment operating parameters are mismatched with the current paper conditions, it is easy to confuse these with mechanical faults, leading to misdiagnosis, redundant maintenance, and low maintenance efficiency. Therefore, how to identify paper feeding anomalies in advance and accurately distinguish between different causes of anomalies for targeted maintenance has become an urgent problem to be solved in the field of intelligent maintenance of printing equipment. Summary of the Invention

[0005] To address this, the present invention provides a predictive maintenance method for printing equipment based on big data analysis. This method overcomes the problem in existing technologies where it is difficult to identify minute abnormal changes in the paper feeding process in a timely manner. As a result, paper feeding abnormalities are often only discovered after paper jams, machine shutdowns, or printing quality defects occur, and the technology lacks predictive maintenance capabilities. In addition, existing technologies lack comprehensive analysis of factors such as paper type, storage condition, and workshop environment, making it difficult to distinguish between different abnormal causes such as mechanical failure, paper moisture absorption, dust contamination, and mismatch of operating parameters. This can easily lead to misjudgments, insufficient targeted maintenance, and low maintenance efficiency.

[0006] To achieve the above objectives, the present invention provides a predictive maintenance method for printing equipment based on big data analysis, comprising: Acquire paper feeding status data, production process data, and environmental data during the paper feeding process of printing equipment; Based on the paper feeding status data, determine the micro-delay characteristic parameters of the paper in the paper feeding path; The micro-delay characteristic parameters include the effective micro-delay amount and the location where the delay occurs on a single sheet of paper; The printing equipment is initially assessed based on the micro-delay characteristic parameters to determine its operating status. In response to the printing equipment being in an uncertain abnormal state of paper feeding, a secondary determination is made by combining the production process data and environmental data to determine the working status of the printing equipment. The working states include normal paper feeding, abnormal paper feeding, and uncertain paper feeding. The abnormal paper feeding states include abnormal adsorption paper supply and abnormal mechanical handover. The abnormal paper feeding states include abnormal sheet moisture absorption, abnormal dust pollution, and abnormal working condition adaptation. Develop corresponding maintenance strategies based on the operating status of the printing equipment; The maintenance strategy includes restoring the airflow and negative pressure stability of the feeder paper suction mechanism, restoring the mechanical handover synchronization, restoring the stability of the paper environment, restoring the cleanliness of the paper feeding path, and re-establishing the dynamic matching between equipment parameters and paper operating conditions.

[0007] As a preferred technical solution for predictive maintenance of printing equipment based on big data analysis, the paper feeding status data includes paper arrival time, front guide positioning signal and paper feed roller speed; the production process data includes paper type and paper storage time; and the environmental data includes workshop humidity.

[0008] As a preferred technical solution for predictive maintenance of printing equipment based on big data analysis, the process of determining the micro-delay characteristic parameters of paper in the paper feeding path based on the paper feeding status data includes: The theoretical arrival time of the paper at each feeding position in the paper feeding path is determined based on the paper arrival time, the front guide positioning signal, and the paper feeding roller speed. The difference between the actual arrival time and the theoretical arrival time of each paper feeding position is determined as the local micro-delay of the corresponding paper at that paper feeding position; The effective micro-delay of a single sheet of paper is determined based on the local micro-delay at each paper feeding position. The location of the delay is determined based on the paper feeding sequence and the delay position corresponding to each effective micro-delay amount of a single sheet of paper; The paper feeding positions include the feeder suction section, the pre-positioning section, the paper handover section, and the paper conveying roller section.

[0009] As a preferred technical solution for predictive maintenance of printing equipment based on big data analysis, the local micro-delay is determined to be an effective micro-delay based on the determination result that the local micro-delay is greater than the preset delay of the corresponding paper feeding position. The preset delay amount is related to the paper feeding sequence at the corresponding paper feeding position.

[0010] As a preferred technical solution for predictive maintenance of printing equipment based on big data analysis, a preliminary judgment is made on the printing equipment based on the micro-delay characteristic parameters, wherein: Obtain the effective micro-delay amount and delay occurrence position corresponding to a preset number of consecutive sheets, and determine the sheets with at least one effective micro-delay amount as delayed sheets; The number of delayed papers in a continuously preset quantity of papers is counted to determine the proportion of delayed papers. The paper feeding status of the printing equipment is determined based on the relationship between the delayed paper ratio and the preset paper ratio. In response to a situation where the paper is not in normal feeding condition, the number of times the delay location of each delayed paper occurs at different paper feeding positions is counted, and the paper feeding position with the most occurrences is determined as the main delay location. The proportion of delay concentration is determined by the ratio of the frequency of occurrence of the main delay position to the number of delay papers; The operating status is determined based on the main delay location and the corresponding delay concentration ratio, wherein: Based on the determination result that the proportion of the delay concentration is not greater than the preset concentration proportion, the working state is determined to be the paper feeding uncertainty state. Based on the determination result that the concentration ratio of the delay is greater than the preset concentration ratio, and combined with the main delay position, the working state is determined, including: If the main delay position is the feeder suction section, then the printing equipment is determined to be in an abnormal suction paper feeding state. If the main delay position is not the feeder suction section, then the printing equipment is determined to be in a mechanical handover abnormal state.

[0011] As a preferred technical solution for predictive maintenance of printing equipment based on big data analysis, in response to the printing equipment being in an uncertain paper feeding state, a secondary determination is made by combining the production process data and environmental data, including: The humidity sensitivity coefficient is determined based on the paper type, and the humidity influence parameters are determined based on the humidity sensitivity coefficient, paper storage time, and workshop humidity. Determine the delay variation parameter based on the effective micro-delay amount of a continuously preset number of papers; A secondary determination is made based on the humidity influence parameter and the delayed change parameter, wherein: If the humidity influence parameter is greater than the humidity influence threshold and the delay change parameter is greater than the delay change threshold, then the paper is determined to be in an abnormal moisture absorption state. Conversely, the diffusion direction is determined based on the location of the delay in the occurrence of a continuously preset number of papers to determine whether it is an abnormal state of dust pollution.

[0012] As a preferred technical solution for predictive maintenance of printing equipment based on big data analysis, the diffusion direction is determined by analyzing the location of the delay in the occurrence of a continuously preset number of sheets of paper to determine whether it is an abnormal state of dust contamination, including: If the diffusion direction is from the non-feeder suction section to the feeder suction section, it is determined to be an abnormal dust pollution state. Conversely, it is determined to be an abnormal working condition.

[0013] As a preferred technical solution for predictive maintenance of printing equipment based on big data analysis, a corresponding maintenance strategy is determined based on the operating status of the printing equipment, including: In response to abnormal paper feeding conditions, maintenance strategies include restoring the airflow stability and negative pressure stability of the feeder paper feeding mechanism; In response to abnormal mechanical handover conditions, the maintenance strategy includes restoring mechanical handover synchronization. In response to abnormal paper moisture absorption, the maintenance strategy includes restoring the stability of the paper environment. In response to abnormal dust contamination conditions, maintenance strategies include restoring the cleanliness of the paper feed path; In response to abnormal operating conditions, the maintenance strategy includes re-establishing a dynamic match between equipment parameters and paper operating conditions.

[0014] As a preferred technical solution for predictive maintenance of printing equipment based on big data analysis, the process of determining humidity-related parameters includes: Based on the paper type, the corresponding humidity sensitivity coefficient, reference humidity, and reference storage time are determined by matching them in the paper type correspondence table; The humidity deviation value is determined based on the workshop humidity and the reference humidity; The cumulative storage value is determined based on the paper storage time and the baseline storage time. The humidity effect coefficient is calculated by weighted summation of the humidity deviation value and the storage accumulation value. The product of the humidity effect coefficient and the humidity sensitivity coefficient is determined as the humidity influence parameter.

[0015] As a preferred technical solution for predictive maintenance of printing equipment based on big data analysis, when the workshop humidity is not greater than the reference humidity, the humidity deviation value is recorded as zero; when the paper storage time is not greater than the reference storage time, the storage accumulation value is recorded as zero.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a predictive maintenance method for printing equipment based on big data analysis. By collecting paper feeding status data, production process data, and environmental data during the paper feeding process of the printing equipment, and combining the micro-delay characteristic parameters of the paper in the paper feeding path, it achieves layered judgment and predictive maintenance of the printing equipment's operating status. This invention can identify paper feeding anomaly trends in advance before they develop into serious paper jams, registration misalignment, or machine shutdown failures, thereby improving the operational stability and production continuity of the printing equipment. First, based on the difference between the theoretical arrival time and the actual arrival time of the paper at each paper feeding position, an effective micro-delay amount and the location of delay occurrence are constructed, thereby achieving refined identification of paper feeding cycle anomalies. Second, the normal paper feeding state and the concentrated delay ratio are used to determine the paper feeding cycle anomalies. The stratified judgment of paper abnormalities and uncertain paper feeding states can identify not only concentrated faults such as feeder paper suction abnormalities and mechanical handover abnormalities, but also complex coupled abnormalities such as paper moisture absorption, dust diffusion, and operating condition adaptation deviations, thus improving the accuracy of abnormality identification. Furthermore, this invention introduces production process data and environmental data into the secondary judgment process. Through the analysis of humidity influence parameters, delay change parameters, and diffusion direction, it achieves the distinction between abnormalities caused by environmental and process factors, avoiding the problem of misjudgment based solely on mechanical status in traditional solutions. Finally, this invention establishes differentiated maintenance strategies for different abnormal states, ensuring that maintenance actions correspond to the abnormality formation mechanism, thereby improving maintenance targeting, reducing the frequency of ineffective maintenance, minimizing equipment downtime, and gradually forming an optimization mechanism for operating parameters under different paper conditions, demonstrating high practical application value. In particular, establishing a paper feeding anomaly judgment mechanism based on micro-delay feature parameters enables early identification of paper feeding anomalies in printing equipment, improving anomaly detection accuracy and predictive capability. This invention calculates the difference between the theoretical and actual arrival times of the paper at each feeding position by acquiring the paper arrival time, the front guide positioning signal, and the paper feeding roller speed. It further forms local micro-delay quantities and effective micro-delay quantities, thus enabling the identification of paper feeding cycle change trends before significant paper jams or machine stoppages occur. Simultaneously, this invention further combines the proportion of delayed paper and the proportion of concentrated delays for a continuously preset number of papers to achieve layered judgment of anomaly states. The proportion of concentrated delays not only reflects whether paper feeding anomalies occur in a concentrated manner but also whether the source of the anomaly has a clear mechanical orientation, thereby enabling the differentiation between adsorption-based paper feeding anomalies and mechanical handover anomalies. Compared to traditional methods that rely solely on single-point alarms for fault judgment, this invention can identify the anomaly propagation pattern from the perspective of continuous changes in the paper feeding cycle, improving the stability and accuracy of anomaly identification and reducing false alarms caused by occasional paper feeding fluctuations. In particular, by incorporating production process data and environmental data into the paper feeding anomaly analysis process, this invention enables precise differentiation of complex paper feeding anomalies such as paper moisture absorption anomalies, dust contamination anomalies, and operating condition mismatch anomalies, thereby improving the targeting and efficiency of maintenance strategies. Under uncertain paper feeding conditions, this invention further combines paper type, paper storage time, and workshop humidity to determine humidity impact parameters, and analyzes the degree of paper moisture absorption impact by combining the delayed change parameters of continuous paper, thus identifying paper feeding instability problems caused by paper moisture absorption. Simultaneously, by analyzing the diffusion direction of the delayed occurrence location of continuous paper, it determines whether the anomaly is caused by dust contamination diffusion or whether it belongs to a mismatch between equipment operating parameters and the current paper operating conditions. Therefore, this invention can differentiate between different anomaly causes, improving the ability to identify complex paper feeding anomalies. In particular, this invention establishes corresponding maintenance strategies for different abnormal states: for abnormal paper feeding states, the focus is on restoring the airflow stability and negative pressure stability of the feeder paper suction mechanism; for abnormal mechanical handover states, the focus is on restoring the synchronization of mechanical handover; and for abnormal paper moisture absorption states, the focus is on restoring the stability of the paper environment. This maintenance method ensures that maintenance actions correspond to the mechanism of abnormality, avoiding the waste of maintenance resources caused by traditional uniform maintenance modes, improving equipment recovery efficiency, and reducing equipment downtime, thus demonstrating good industrial application effects. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the predictive maintenance method for printing equipment based on big data analysis, as described in an embodiment of the present invention. Figure 2 This is a flowchart illustrating the initial determination process of the printing equipment in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0019] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0020] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0021] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0022] Please see Figure 1 The diagram shown is a flowchart of a predictive maintenance method for printing equipment based on big data analysis, according to an embodiment of the present invention. This embodiment of the present invention provides a predictive maintenance method for printing equipment based on big data analysis, comprising: Step S1: Obtain paper feeding status data, production process data, and environmental data during the paper feeding process of the printing equipment; Specifically, the paper feeding status data includes paper arrival time, front guide positioning signal, and paper feed roller speed; the production process data includes paper type and paper storage time; and the environmental data includes workshop humidity.

[0023] It should be understood that the paper feeding process in printing equipment refers to the continuous conveying process of paper after it is picked up by the feeder, through the pre-positioning stage, the paper gripping and handover stage, and the paper conveying roller stage before entering the printing station. This paper feeding process can be divided into the feeder suction section, the pre-positioning stage, the paper gripping and handover stage, and the paper conveying roller stage according to the paper feeding path.

[0024] Paper feeding status data refers to data used to characterize the running status of paper in the paper feeding path. It reflects whether the paper arrives at the corresponding paper feeding position according to the predetermined rhythm. It includes paper arrival time, front guide positioning signal, and paper feeding roller speed: Paper arrival time refers to the time point when the paper arrives at the preset paper feeding position detected by photoelectric sensors, proximity sensors, vision inspection devices, or other paper arrival detection devices. The preset paper feeding position includes one or more of the feeder suction section, front guide positioning section, gripper handover section, or paper feeding roller conveying section; Front guide positioning signal refers to the detection signal generated when the leading edge of the paper reaches the front guide positioning position and completes positioning. This signal can be generated by the positioning sensor, position switch, or vision inspection device at the front guide and is used to characterize whether the paper arrives at the front guide positioning section according to the equipment's set rhythm; Paper feeding roller speed refers to the rotational speed of the paper feeding roller during the paper feeding process or the linear speed converted from the rotational speed. The paper feeding roller speed can be read by encoders, speed sensors, or equipment control systems and is used to calculate the theoretical conveying time of the paper between adjacent paper feeding positions.

[0025] Production process data refers to data related to the current printing task and paper process conditions. It is used to characterize the impact of the paper itself and its usage status on paper feeding stability, including paper type and paper storage time. Paper type refers to the category or attribute of the paper used in the current printing task, including but not limited to coated paper, offset paper, card stock, thin paper, laminated paper, or paper categories with different basis weights, thicknesses, stiffness, and surface roughness. Different paper types correspond to different moisture absorption sensitivities and paper feeding adaptability. Paper storage time refers to the duration of time that the paper is stored in the workshop, paper warehouse, or near the printing equipment before entering the current printing operation. Paper storage time can be calculated from the time of unpacking, storage, machine start-up, or manually entered storage start time, and is used to reflect the cumulative degree of influence of environmental humidity on the paper.

[0026] Environmental data refers to data that may affect the stability of paper feeding in the working environment of printing equipment. It is used to characterize the impact of the external environment on the paper condition and the operating status of the paper feeding mechanism, including workshop humidity and dust concentration. Workshop humidity refers to the relative humidity of the air in the workshop where the printing equipment is located or near the paper feeding mechanism. Workshop humidity can be collected in real time by humidity sensors to determine whether the paper may deform, curl, stick or change its frictional properties due to moisture absorption.

[0027] It should be understood that the paper feeding path is divided into four feeding positions according to the direction of paper movement: feeder suction section, front positioning section, gripper handover section, and paper conveyor section. Each feeding position can be set with a corresponding paper arrival detection point, and the time for the paper to arrive at that feeding position can be calculated by the signals of adjacent sensors, encoder phase, and paper conveyor speed. Therefore, the same sheet of paper can correspond to the arrival time of the feeder suction section, the arrival time of the front positioning section, the arrival time of the gripper handover section, and the arrival time of the paper conveyor section during the paper feeding process. Step S2, determining the micro-delay characteristic parameters of the paper in the paper feeding path based on the paper feeding status data, specifically includes: Step S21: Determine the theoretical arrival time of the paper at each feeding position in the paper feeding path based on the paper arrival time, the front guide positioning signal, and the paper feeding roller speed. It can be understood that the theoretical arrival time refers to the time it should take for the paper to reach a certain feeding position under normal paper feeding cycle time of the printing equipment. It is not the actual time detected by the sensor, but a standard arrival time calculated based on the equipment cycle time, the front guide positioning signal, and the paper feeding roller speed. In implementation, the reference cycle time corresponding to the current paper is used as the starting time, and the theoretical arrival time is determined by combining the path distance between the feeding positions and the paper feeding roller speed. Typically: Theoretical arrival time = Starting time + Distance between a certain feeding position and the paper starting point ÷ Paper feeding roller speed. The reference cycle time is determined based on the front guide positioning signal and the equipment feeding cycle. When the front guide positioning signal indicates that the j-th sheet should enter the front guide positioning cycle, the cycle time corresponding to this signal can be used as the reference cycle time for the j-th sheet, or the paper feeding cycle in the equipment control system can be calibrated based on the front guide positioning signal. Step S22: The difference between the actual arrival time and the theoretical arrival time of each paper feeding position is determined as the local micro-delay of the corresponding paper at that paper feeding position. It should be understood that the local micro-delay refers to the difference between the actual arrival time and the theoretical arrival time of the same paper at a specific paper feeding position. It is a local time deviation of a certain paper feeding position. The local micro-delay of a certain paper feeding position = the actual arrival time of the paper feeding position - the theoretical arrival time of the paper feeding position. If the local micro-delay > 0, it means that the paper arrives at the paper feeding position later than the theoretical time. If < 0, it means that there is no possibility of paper jam when the theoretical time arrives. Step S23: Determine the effective micro-delay of the paper based on the local micro-delay of the single sheet at each paper feeding position; Specifically, based on the determination result that the local micro-delay is greater than the preset delay at the corresponding paper feeding position, the local micro-delay is determined to be an effective micro-delay. The preset delay amount is related to the paper feeding sequence at the corresponding paper feeding position; It is understandable that the motion characteristics of different paper feeding positions are different and the paper delay may accumulate and propagate along the paper feeding direction. Therefore, a corresponding preset delay amount is set for each paper feeding position. Specifically, this includes the preset delay amount for the feeder suction section, the preset delay amount for the preceding positioning section, the preset delay amount for the paper gripping handover section, and the preset delay amount for the paper feeding roller conveying section. Since the delay detected in the later section after the paper is conveyed segment by segment along the paper feeding path may include the cumulative error transmitted from the previous section, the preset delay amount is in an increasing relationship with the paper feeding sequence. In one implementation, the preset delay amount for the feeder suction section, the preset delay amount for the preceding positioning section, the preset delay amount for the paper gripping handover section, and the preset delay amount for the paper feeding roller conveying section are calibrated according to the equipment model and historical operating data, and are usually set to 5ms, 8ms, 10ms, and 12ms, respectively. Step S24: Determine the delay occurrence position based on the paper feeding sequence and the delay position corresponding to each effective micro-delay amount of a single sheet of paper. It can be understood that the delay occurrence position refers to the paper feeding position where the effective micro-delay amount first appears during the paper feeding process of the same sheet. The paper feeding sequence should be considered when determining the delay occurrence position. In practice, if a single sheet of paper has one effective micro-delay amount, the delay position corresponding to that effective micro-delay amount is determined as the delay occurrence position of that sheet. If a single sheet of paper has multiple effective micro-delay amounts, the delay position corresponding to the first effective micro-delay amount is determined as the delay occurrence position of that sheet according to the paper feeding sequence of feeder suction section, pre-positioning section, gripper handover section, and paper feeding roller conveying section.

[0028] Please see Figure 2 As shown, it is a flowchart of the initial determination process of the printing equipment in an embodiment of the present invention.

[0029] Step S3, performing a preliminary determination of the printing equipment based on the micro-delay characteristic parameters to determine the operating status of the printing equipment, including: Step S31: Obtain the effective micro-delay amount and delay occurrence position corresponding to the consecutive preset number of papers, and determine the paper with at least one effective micro-delay amount as the delayed paper; In practice, the preset number is usually 5 to 10 sheets of paper; If the consecutive preset number is too small, it is easily affected by occasional factors such as single sheet warping, uneven paper edges, and sensor jitter, which may lead to false alarms. It is understandable that if a single sheet of paper has no effective micro-delay, it means that the paper is running basically according to the theoretical cycle time at each paper feeding position. If there is at least one effective micro-delay, it means that the paper has a identifiable cycle time deviation at at least one position in the paper feeding path, and therefore it can be regarded as a delayed paper. Step S32: Count the number of delayed papers in the consecutive preset number of papers to determine the proportion of delayed papers; it can be understood that the proportion of delayed papers = number of delayed papers ÷ preset number; Step S33: Determine whether the printing equipment is in normal paper feeding condition based on the relationship between the delayed paper ratio and the preset paper ratio, wherein: If the proportion of delayed paper is not greater than the preset paper proportion, the printing equipment is determined to be in normal paper feeding condition; otherwise, it is determined to be in abnormal paper feeding condition. It can be understood that if the proportion of paper with effective micro-delay in a continuous preset number of papers is low, it indicates that the overall paper feeding cycle is stable and the micro-delay is more likely to be an occasional fluctuation. If the proportion of paper with effective micro-delay is high, it indicates that the micro-delay has become continuous and the source of the abnormality needs to be further determined. In practice, the preset paper ratio can be 30% to 50%, preferably 45%; Step S34: In response to the paper feeding abnormality, count the number of times the delay location of each delayed paper occurs at different paper feeding positions, and determine the paper feeding position with the most occurrences as the main delay location. It can be understood that if the earliest effective micro-delay of multiple delayed papers is concentrated at the same paper feeding position, it indicates that the paper feeding position is more likely to be the main location of the paper feeding abnormality. Step S35: Determine the delay concentration ratio based on the ratio of the number of occurrences of the main delay position to the number of delayed papers. In practice, the delay concentration ratio = the number of occurrences of the main delay position ÷ the number of delayed papers. It can be understood that a high delay concentration ratio indicates that the delay source of most delayed papers is concentrated in the same paper feeding position, and the anomaly can be classified as a confirmed anomaly. If the delay concentration ratio is low, it indicates that the delay occurs in a dispersed manner and the source of the anomaly is unstable, and it is not appropriate to directly attribute it to a specific institution. Step S36: Determine the working state based on the main delay position and the corresponding delay concentration ratio, wherein: Based on the determination result that the concentration ratio of the delay is not greater than the preset concentration ratio, the working state is determined to be the paper feeding uncertainty state. In practice, the preset concentration ratio can be 60% to 85%, preferably 70%. If the preset concentration ratio is set too low, it may still be forcibly classified as a definite anomaly when the delay position is not stable. If it is set too high, only the position distribution with a high degree of consistency will enter the definite anomaly, which may lead to too many anomalies being classified into the uncertainty state. It is understandable that although the proportion of delayed paper has exceeded the preset proportion of paper, indicating an abnormal trend in paper feeding, the location of the delay is not obviously concentrated. The source of the abnormality may be affected by paper humidity, paper storage status, paper type, or the coordinated fluctuation of multiple paper feeding stages. It cannot be directly classified as an adsorption paper feeding abnormality or mechanical handover abnormality in the initial judgment stage. Based on the determination result that the concentration ratio of the delay is greater than the preset concentration ratio, and combined with the main delay position, the working state is determined, including: If the main delay location is the feeder suction section, the printing equipment is determined to be in an abnormal paper feeding state. At this time, micro-delays continue to occur, and the source of the delay is stably concentrated at the paper feeding start end. It can be understood that the feeder suction section is located at the start end of the paper feeding path and is mainly responsible for paper suction, separation and initial paper feeding. If the delay location of most delayed papers is concentrated in the feeder suction section, it means that the micro-delay has already occurred when the paper just enters the paper feeding path. Therefore, the source of the abnormality is more likely to be related to the feeder suction section, such as insufficient suction of the nozzle, nozzle blockage, insufficient paper separation, feeder cycle deviation or unstable paper feeding. If the main delay position is a non-feeder suction section, the printing equipment is determined to be in a mechanical handover abnormal state. At this time, micro-delays continue to occur, and the source of the delay is stably concentrated in the paper feeding mechanism after the feeder suction section. It can be understood that the non-feeder suction section includes the pre-positioning section, the paper gripping handover section, and the paper feeding roller conveying section. The fact that the delay position is concentrated in the paper feeding position after the feeder indicates that the initial paper suction and feeding process is relatively stable, but there is a deviation in the rhythm during the subsequent positioning, handover, or conveying process. During implementation, when the main delay position is the previous specified positioning section, it may correspond to the previous specified positioning deviation, unstable paper guide, or abnormal positioning cycle. When the main delay position is the gripper handover section, it may correspond to abnormal gripper opening and closing timing, gripper pressure, or handover phase. When the main delay position is the paper feed roller conveying section, it may correspond to insufficient paper feed roller pressure, paper feed roller slippage, or abnormal transmission synchronization.

[0030] Step S4, in response to the printing equipment being in an uncertain paper feeding abnormal state, a secondary determination is made based on the production process data and environmental data to determine the operating status of the printing equipment, including: Step S41: Determine the corresponding humidity sensitivity coefficient based on the paper type, and determine the humidity influence parameter based on the humidity sensitivity coefficient, paper storage time, and workshop humidity. It is understood that different papers have different sensitivities to humidity. Thin paper, offset paper, uncoated paper, or highly absorbent paper are more likely to absorb moisture, curl, stick together, or change their coefficient of friction due to increased workshop humidity and prolonged storage time. Coated paper, art paper, or paper with more stable surface treatment are relatively less sensitive to humidity changes. Step S42: Determine the delay variation parameter based on the effective micro-delay of a preset number of consecutive sheets. In practice, the delay variation parameter is determined based on the growth rate of the effective micro-delay of a preset number of consecutive sheets. The delay variation parameter = (average effective micro-delay in the second half - average effective micro-delay in the first half) ÷ average effective micro-delay in the first half. It should be understood that the delay variation parameter is used to characterize whether the micro-delay of the consecutive sheets shows an increasing trend. If the effective micro-delay of the consecutive sheets gradually increases, it indicates that the paper feeding abnormality is not an occasional fluctuation, but shows an increasing trend in a short period of time, which is more likely to be related to changes in the environment or paper condition. Step S43, perform a secondary determination based on the humidity influence parameter and the delayed change parameter, wherein: If the humidity-affecting parameter is greater than the humidity-affecting threshold and the delay change parameter is greater than the delay change threshold, it is determined to be an abnormal paper moisture absorption state. It is understood that high humidity alone does not necessarily lead to paper feeding abnormalities, and a slight increase in micro-delay alone does not necessarily indicate that the paper is absorbing moisture. Increased humidity in the workshop, longer paper storage time, and the paper type itself being moisture-sensitive will cause changes in the paper's moisture content, which in turn will cause paper curling, wavy edges, changes in surface friction, or adhesion between papers. These changes will increase the resistance to the movement of the paper in the paper feeding path, which will manifest as a gradual increase in the effective micro-delay of continuous paper. In practice, the humidity influence parameter is normalized and its value range is 0 to 1. The humidity influence threshold can be 0.25 to 0.5, preferably 0.35. The delay change threshold can be 10% to 30%, preferably 15%, to avoid identifying the detection of shaking or single-sheet edge differences as abnormal growth or failing to trigger secondary judgment when the signs of paper jam have already appeared. Conversely, the diffusion direction is determined based on the location of the delay in the occurrence of a preset number of sheets of paper to determine whether it is an abnormal dust pollution state, including: If the diffusion direction is from the non-feeder suction section to the feeder suction section, it is determined to be an abnormal dust contamination state. It can be understood that, within the continuous monitoring window, if the location of the delay gradually migrates from the non-feeder suction section such as the pre-positioning section, the paper pick-up section, or the paper conveying section of the paper feed roller to the feeder suction section, it indicates that the source of the delay is gradually concentrating at the paper feeding start end. Since dust easily adheres to the nozzle, paper surface, or feeder suction area, resulting in decreased adsorption force, insufficient paper separation, or unstable paper lifting, it can be determined to be an abnormal dust contamination state. Conversely, it is judged as an abnormal state of working condition adaptation. It is understandable that when the humidity effect parameter is insufficient to explain the micro-delay change, and the location of the delay does not migrate to the feeder paper suction section, it indicates that the current paper feeding uncertainty may not be dominated by paper moisture absorption or dust contamination, but by the mismatch between the current production conditions and the equipment paper feeding cycle.

[0031] Specifically, step S41 includes: Step S411: Based on the paper type, determine the corresponding humidity sensitivity coefficient, reference humidity, and reference storage time by matching them in the paper type correspondence table; Table 1 is the paper type correspondence table: Table 1. Correspondence between paper types During implementation, the values ​​in Table 1 are dynamically adjusted based on paper supplier parameters, historical production data, or on-site calibration results; Understandably, the baseline humidity refers to the allowable reference workshop humidity for this type of paper under normal paper feeding conditions; the baseline storage time refers to the reference storage time for this type of paper in the current workshop environment where it is not easily affected by significant moisture absorption; the humidity sensitivity coefficient is used to characterize the sensitivity of paper type to changes in environmental humidity: the larger the humidity sensitivity coefficient, the more easily this type of paper is affected by workshop humidity and storage time. Thin paper, offset paper, and hygroscopic specialty paper have higher moisture absorption sensitivity and larger humidity sensitivity coefficients, while laminated paper has stronger surface barrier properties and smaller humidity sensitivity coefficients. Step S412: Determine the humidity deviation value based on the workshop humidity and the reference humidity; in practice, the humidity deviation value = max[0,(workshop humidity-reference humidity)÷(100-reference humidity)], and only when the workshop humidity exceeds the reference humidity corresponding to the paper type can it have an adverse effect on paper transport; Step S413: Determine the cumulative storage value based on the paper storage time and the baseline storage time; In practice, the cumulative storage value = max[0, (actual storage time - baseline storage time) ÷ baseline storage time]. The longer the paper is exposed to the workshop environment, the more easily it is affected by moisture accumulation. If the storage time has not exceeded the baseline storage time, its moisture accumulation effect is considered to be weak. Step S414: Calculate the humidity effect coefficient by weighted summation of the humidity deviation value and the accumulated storage value; in practice, the humidity effect parameter = first weight × humidity deviation value + second weight × accumulated storage value, where: first weight + second weight = 1, first weight ≥ second weight, preferably: the first weight is set to 0.6, and the second weight is set to 0.4; Step S415: The product of the humidity effect coefficient and the humidity sensitivity coefficient is determined as the humidity influence parameter; In practice, the humidity influence parameter can usually fall between 0 and 1.5. Based on this, the humidity influence parameter is normalized to obtain the final humidity influence parameter. Wherein, when the workshop humidity is not greater than the reference humidity, the humidity deviation value is recorded as zero; when the paper storage time is not greater than the reference storage time, the storage accumulation value is recorded as zero. It is understandable that the humidity impact threshold is a normalized threshold with the same dimensions as the humidity impact parameter.

[0032] Step S5: Based on the abnormal state or cause of the printing equipment, formulate corresponding maintenance strategies, including: In response to abnormal paper feeding conditions, it indicates that the adsorption capacity of the feeder's paper suction section has decreased, resulting in unstable paper separation or unstable paper feeding. Common causes include nozzle blockage, vacuum leakage, insufficient negative pressure of the air pump, or unstable adsorption airflow. However, the essence of all these is insufficient negative pressure adsorption capacity. Therefore, the focus of maintenance is to restore the airflow stability and negative pressure stability of the feeder paper suction mechanism. Specific maintenance strategies include nozzle cleaning to restore the adsorption channel, vacuum pipeline testing to check for leaks, blockages, or airflow attenuation, and air pump maintenance to restore negative pressure output capacity. The abnormal mechanical handover indicates that there is a cycle time deviation or positional deviation in the mechanical handover process of paper in the paper feeding path. Common causes include gripper opening and closing timing deviation, front guide positioning error, uneven pressure of paper feeding rollers, and insufficient synchronization at the moment of paper handover. The essence is the mismatch of mechanical motion. Therefore, the focus of maintenance is to restore the phase consistency, positioning consistency, and pressure consistency between each paper feeding mechanism. Maintenance strategies include gripper phase correction to restore handover timing, front guide detection to restore paper positioning accuracy, and paper feeding roller pressure adjustment to restore paper conveying stability. In response to the abnormal moisture absorption of paper, it indicates that the paper has absorbed moisture due to environmental humidity or prolonged exposure, resulting in changes in the paper's physical properties. Therefore, the focus of maintenance should be on controlling the environment in which the paper is located and the moisture content of the paper. Thus, maintenance strategies include workshop dehumidification to reduce environmental humidity, paper pretreatment to balance the paper's moisture content, and adjustment of the paper stack settling time to prevent the paper from continuously absorbing moisture in a high-humidity environment. In response to the abnormal dust pollution status, it indicates that dust adheres to the feeder paper suction mechanism or the paper feeding path surface, resulting in a decrease in adsorption capacity or abnormal paper feeding friction. Therefore, the focus of maintenance should be on reducing dust adhesion and dust transmission, rather than adjusting mechanical parameters. Maintenance strategies include cleaning the suction nozzle to restore the adsorption contact surface, removing dust from the paper feeding path to restore the stability of the conveying contact, and controlling dust in the workshop to reduce pollution sources. In response to an abnormal operating condition, it indicates that there is a mismatch between the current equipment operating parameters and the current paper operating conditions. Therefore, the dynamic matching between the equipment parameters and the paper operating conditions should be re-established. Maintenance strategies include printing speed downgrading to reduce paper feeding load and feeder cycle time compensation to improve paper feeding synchronization.

[0033] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0034] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A predictive maintenance method for printing equipment based on big data analysis, characterized in that, include: Acquire paper feeding status data, production process data, and environmental data during the paper feeding process of printing equipment; Based on the paper feeding status data, determine the micro-delay characteristic parameters of the paper in the paper feeding path; The micro-delay characteristic parameters include the effective micro-delay amount and the location where the delay occurs on a single sheet of paper; The printing equipment is initially assessed based on the micro-delay characteristic parameters to determine its operating status. In response to the printing equipment being in an uncertain abnormal state of paper feeding, a secondary determination is made by combining the production process data and environmental data to determine the working status of the printing equipment. The working states include normal paper feeding, abnormal paper feeding, and uncertain paper feeding. The abnormal paper feeding states include abnormal adsorption paper supply and abnormal mechanical handover. The abnormal paper feeding states include abnormal sheet moisture absorption, abnormal dust pollution, and abnormal working condition adaptation. Develop corresponding maintenance strategies based on the operating status of the printing equipment; The maintenance strategy includes restoring the airflow and negative pressure stability of the feeder paper suction mechanism, restoring the mechanical handover synchronization, restoring the stability of the paper environment, restoring the cleanliness of the paper feeding path, and re-establishing the dynamic matching between equipment parameters and paper operating conditions.

2. The predictive maintenance method for printing equipment based on big data analysis according to claim 1, characterized in that, The paper feeding status data includes paper arrival time, front guide positioning signal, and paper feed roller speed; the production process data includes paper type and paper storage time; and the environmental data includes workshop humidity.

3. The predictive maintenance method for printing equipment based on big data analysis according to claim 2, characterized in that, The process of determining the micro-delay characteristic parameters of the paper in the paper feeding path based on the paper feeding status data includes: The theoretical arrival time of the paper at each feeding position in the paper feeding path is determined based on the paper arrival time, the front guide positioning signal, and the paper feeding roller speed. The difference between the actual arrival time and the theoretical arrival time of each paper feeding position is determined as the local micro-delay of the corresponding paper at that paper feeding position; The effective micro-delay of a single sheet of paper is determined based on the local micro-delay at each paper feeding position. The location of the delay is determined based on the paper feeding sequence and the delay position corresponding to each effective micro-delay amount of a single sheet of paper; The paper feeding positions include the feeder suction section, the pre-positioning section, the paper handover section, and the paper conveying roller section.

4. The predictive maintenance method for printing equipment based on big data analysis according to claim 3, characterized in that, Based on the determination result that the local micro-delay is greater than the preset delay amount of the corresponding paper feeding position, the local micro-delay is determined to be an effective micro-delay amount; The preset delay amount is related to the paper feeding sequence at the corresponding paper feeding position.

5. The predictive maintenance method for printing equipment based on big data analysis according to claim 3, characterized in that, A preliminary determination of the printing equipment is made based on the micro-delay characteristic parameters, wherein: Obtain the effective micro-delay amount and delay occurrence position corresponding to a preset number of consecutive sheets, and determine the sheets with at least one effective micro-delay amount as delayed sheets; The number of delayed papers in a continuously preset quantity of papers is counted to determine the proportion of delayed papers. The paper feeding status of the printing equipment is determined based on the relationship between the delayed paper ratio and the preset paper ratio. In response to a situation where the paper is not in normal feeding condition, the number of times the delay location of each delayed paper occurs at different paper feeding positions is counted, and the paper feeding position with the most occurrences is determined as the main delay location. The proportion of delay concentration is determined by the ratio of the frequency of occurrence of the main delay position to the number of delay papers; The operating status is determined based on the main delay location and the corresponding delay concentration ratio, wherein: Based on the determination result that the proportion of the delay concentration is not greater than the preset concentration proportion, the working state is determined to be the paper feeding uncertainty state. Based on the determination result that the concentration ratio of the delay is greater than the preset concentration ratio, and combined with the main delay position, the working state is determined, including: If the main delay position is the feeder suction section, then the printing equipment is determined to be in an abnormal suction paper feeding state. If the main delay position is not the feeder suction section, then the printing equipment is determined to be in a mechanical handover abnormal state.

6. The predictive maintenance method for printing equipment based on big data analysis according to claim 5, characterized in that, In response to the printing equipment being in an uncertain paper feeding state, a secondary determination is made based on the aforementioned production process data and environmental data, including: The humidity sensitivity coefficient is determined based on the paper type, and the humidity influence parameters are determined based on the humidity sensitivity coefficient, paper storage time, and workshop humidity. Determine the delay variation parameter based on the effective micro-delay amount of a continuously preset number of papers; A secondary determination is made based on the humidity influence parameter and the delayed change parameter, wherein: If the humidity influence parameter is greater than the humidity influence threshold and the delay change parameter is greater than the delay change threshold, then the paper is determined to be in an abnormal moisture absorption state. Conversely, the diffusion direction is determined based on the location of the delay in the occurrence of a continuously preset number of papers to determine whether it is an abnormal state of dust pollution.

7. The predictive maintenance method for printing equipment based on big data analysis according to claim 6, characterized in that, The diffusion direction is determined based on the location of the delayed occurrence of a preset number of consecutive sheets of paper to determine whether it is an abnormal dust pollution state, including: If the diffusion direction is from the non-feeder suction section to the feeder suction section, it is determined to be an abnormal dust pollution state. Conversely, it is determined to be an abnormal state of working condition adaptation.

8. The predictive maintenance method for printing equipment based on big data analysis according to claim 7, characterized in that, Based on the operating status of the printing equipment, a corresponding maintenance strategy is determined, including: In response to abnormal paper feeding conditions, maintenance strategies include restoring the airflow stability and negative pressure stability of the feeder paper feeding mechanism; In response to abnormal mechanical handover conditions, the maintenance strategy includes restoring mechanical handover synchronization. In response to abnormal paper moisture absorption, the maintenance strategy includes restoring the stability of the paper environment. In response to abnormal dust contamination conditions, maintenance strategies include restoring the cleanliness of the paper feed path; In response to abnormal operating conditions, the maintenance strategy includes re-establishing a dynamic match between equipment parameters and paper operating conditions.

9. The predictive maintenance method for printing equipment based on big data analysis according to claim 6, characterized in that, The process of determining the parameters affecting humidity includes: Based on the paper type, the corresponding humidity sensitivity coefficient, reference humidity, and reference storage time are determined by matching them in the paper type correspondence table; The humidity deviation value is determined based on the workshop humidity and the reference humidity; The cumulative storage value is determined based on the paper storage time and the baseline storage time. The humidity effect coefficient is calculated by weighted summation of the humidity deviation value and the storage accumulation value. The product of the humidity effect coefficient and the humidity sensitivity coefficient is determined as the humidity influence parameter.

10. The predictive maintenance method for printing equipment based on big data analysis according to claim 9, characterized in that, When the humidity in the workshop is not greater than the baseline humidity, the humidity deviation value is recorded as zero; when the storage time of the paper is not greater than the baseline storage time, the cumulative storage value is recorded as zero.

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