Cigarette enterprise equipment health degree calculation method based on multi-dimensional dynamic evaluation

Abstract

The invention discloses a cigarette enterprise equipment health degree calculation method based on multi-dimensional dynamic evaluation, and belongs to the technical field of intelligent operation and maintenance of industrial equipment, and the method comprises the following steps: 1, constructing an equipment health degree hierarchical evaluation model; step 2, free configuration of configuration indexes; step 3, dynamic weight distribution; 4, multi-source data fusion and normalization are carried out; and 5, calculating the overall health degree of the equipment. According to the method, all aspects of equipment management are comprehensively covered through six dimensions, so that the evaluation result is more comprehensive and more accurate; dynamic weight distribution is adopted, so that an evaluation result better fits the actual risk of the equipment; specific secondary evaluation indexes are configured for each dimension in a free configuration mode, so that the method can be flexibly adapted to various equipment in a cigarette factory, the applicability is higher, and the operation and maintenance cost is reduced; and finally, through drilling analysis, operability is improved, and production safety and quality are guaranteed.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of intelligent operation and maintenance of industrial equipment, and particularly relates to a method for calculating the health degree of equipment in a cigarette enterprise based on multi-dimensional dynamic evaluation. BACKGROUND

[0002] In a cigarette industrial enterprise, equipment is the core of production. At present, the management of the enterprise on the equipment mainly depends on periodic maintenance, after-service maintenance and static evaluation based on simple indexes (such as equipment comprehensive efficiency, fault downtime rate, etc.). These methods have obvious disadvantages: first, a single index cannot comprehensively reflect the overall state of the equipment, and a piece of equipment with high comprehensive efficiency may hide a huge bearing wear risk; second, static weight cannot adapt to the characteristics of different equipment (such as the great difference between a cigarette packer and a power fan) and the management focus of the enterprise at different periods; finally, the traditional method cannot systematically integrate management methods such as equipment inspection data, fault tree analysis and failure mode analysis, and it is difficult to realize accurate positioning from the macro health degree to the micro root cause.

[0003] Therefore, there is an urgent need in the art for a quantitative method that can comprehensively, dynamically and accurately evaluate the health status of the equipment in a cigarette enterprise, so as to guide predictive maintenance, reduce operation and maintenance costs, and ensure production safety and quality. SUMMARY

[0004] The purpose of the present application is to provide a method for calculating the health degree of equipment in a cigarette enterprise based on multi-dimensional dynamic evaluation, so as to solve the above technical problems.

[0005] To achieve the above purpose, the present application provides the following technical solution:

[0006] The present application discloses a method for calculating the health degree of equipment in a cigarette enterprise based on multi-dimensional dynamic evaluation, which comprises the following steps:

[0007] Step 1, constructing a health degree hierarchical evaluation model of equipment: a health degree hierarchical evaluation model of equipment in a cigarette enterprise is constructed, which defines the overall health degree of the equipment as the weighted sum of six dimensions, including the health degree of equipment running state, the health degree of equipment efficiency, the health degree of product quality, the health degree of equipment cost, the health degree of process management and the health degree of equipment safety, and the model expression is:

[0008] OEH = W1 × P_Status + W2 × P_Efficiency + W3 × P_Quality + W4 × P_Cost + W5 × P_Innovation + W6 × P_Safety (1)

[0009] wherein, OEH represents the overall health of the equipment; P_Status represents the health of the equipment running state; P_Efficiency represents the health of the equipment efficiency; P_Quality represents the health of the product quality; P_Cost represents the health of the equipment cost; P_Innovation represents the health of the process management; P_Safety represents the health of the equipment safety; W1-W6 are respectively the weight of each dimension, and W1+W2+...+W6=1;

[0010] Step 2, free configuration index: for different types of equipment, the specific secondary evaluation indexes of the six dimensions are configured by free configuration;

[0011] Step 3, dynamic weight distribution: first, the weight of each dimension is adjusted based on the FMEA strategy, that is, the failure mode and effect analysis of the equipment is carried out, the risk coefficient RPN of each failure mode is calculated, and the failure mode is classified into the corresponding dimension, and the weight W1-W6 of each dimension in step 1 is dynamically distributed and adjusted according to the total RPN value under each dimension; then the weight is corrected based on the management strategy;

[0012] Step 4, multi-source data fusion and normalization: multi-source data from SCADA (real-time data), MES (production data), MRO (maintenance data), and ERP (financial data) systems are collected and integrated, so as to obtain specific secondary evaluation indexes of the six dimensions; for secondary evaluation indexes of different dimensions, normalization processing is carried out, and they are uniformly mapped to the score interval of [0, 100];

[0013] Step 5, calculation of overall health of the equipment: according to the normalized secondary index score and its internal weight, the health score of each dimension is calculated in turn; then the overall health of the equipment OEH is calculated by formula (1).

[0014] Further, the secondary evaluation indexes configured for the health of the equipment running state in step 2 include: vibration monitoring, temperature monitoring, pressure monitoring, comprehensive efficiency of the equipment, mean time between failures / mean time to repair; the secondary evaluation indexes configured for the health of the equipment efficiency include: hourly output, time utilization rate, input-output ratio, energy consumption per unit output, raw material consumption rate; the secondary evaluation indexes configured for the health of the product quality include: yield, defect rate, process parameter CPK; the secondary evaluation indexes configured for the health of the equipment cost include: unit output maintenance cost, spare parts capital turnover rate; the secondary evaluation indexes configured for the health of the process management include: maintenance plan execution rate, preventive maintenance proportion; the secondary evaluation indexes configured for the health of the equipment safety include: equipment integrity, equipment service life, equipment newness coefficient.

[0015] Further, the formula for calculating the risk coefficient RPN of each failure mode in step 3 is:

[0016] RPN = S x O x D (2)

[0017] Wherein: S represents severity; O represents occurrence; and D represents detection.

[0018] Further, when the total RPN value under each dimension is used to dynamically allocate and adjust the weight W1 to W6 of each dimension in step 1 in step 3, the higher the total RPN value of a certain dimension, the greater the weight, and for this dimension, the specific secondary evaluation indicators associated therewith are dynamically adjusted.

[0019] Further, the weight correction based on the management strategy in step 3 is specifically: in the cost control period, the quality improvement month, and after the equipment overhaul, the weight of the corresponding dimension is manually adjusted to meet the current management target.

[0020] Further, after the total health degree of the equipment is calculated in step 5, the calculation result is visualized and displayed through the health degree dashboard; and a drill analysis function is provided, so that the user can drill down from the total health degree to a specific low-score dimension, and then drill down to a specific abnormal secondary indicator, and view the corresponding abnormal point inspection record or fault work order to locate the fault cause.

[0021] The method for calculating the health degree of the equipment of the cigarette enterprise based on the multi-dimensional dynamic evaluation has the advantages that: the method fully covers each aspect of equipment management through six dimensions, so that the evaluation result is more comprehensive and more accurate; the dynamic weight allocation is mainly based on the FMEA strategy and supplemented by the management strategy, so that the evaluation result is more in line with the actual risk of the equipment; the specific secondary evaluation indicators are configured for each dimension in a free configuration manner, so that the method can be flexibly adapted to the different equipment in the cigarette factory, has stronger applicability, and reduces the operation and maintenance cost; finally, the abstract "health degree" score can be converted into a specific maintenance action instruction through the drill analysis, so that the team can take accurate actions, the operability is improved, and the production safety and quality are ensured.

[0022] The application will be further described in detail below with reference to the accompanying drawings and specific embodiments. DETAILED DESCRIPTION

[0023] Figure 1 The method flowchart of the application is shown in the figure;

[0024] Figure 2 The device health degree hierarchical evaluation model structure diagram is shown in the figure;

[0025] Figure 3FIG. 1 shows a health visualization dashboard and drill-down analysis interface for an embodiment. DETAILED DESCRIPTION

[0026] The application discloses a cigarette enterprise equipment health degree calculation method based on multi-dimensional dynamic evaluation, and relates to the field of equipment health degree calculation. Figure 1 As shown in the figure, the method comprises the following steps:

[0027] Step 1, constructing an equipment health degree hierarchical evaluation model: constructing a cigarette enterprise equipment health degree hierarchical evaluation model, the model defines the overall equipment health degree as the weighted sum of six dimensions (including equipment running state health degree, equipment efficiency health degree, product quality health degree, equipment cost health degree, process management health degree and equipment safety health degree), that is:

[0028] OEH = W1 × P_Status + W2 × P_Efficiency + W3 × P_Quality + W4 × P_Cost + W5 × P_Innovation + W6 × P_Safety (1)

[0029] Wherein, OEH represents the overall equipment health degree; P_Status represents the equipment running state health degree; P_Efficiency represents the equipment efficiency health degree; P_Quality represents the product quality health degree; P_Cost represents the equipment cost health degree; P_Innovation represents the process management health degree; P_Safety represents the equipment safety health degree; W1~W6 are the weights of each dimension, and W1 + W2+... + W6 = 1.

[0030] The equipment running state health degree is the physical basis and perceptual starting point of the entire equipment health degree evaluation system. It focuses on the real-time running condition and technical performance stability of the equipment itself, and directly reflects whether the equipment is in a "available, reliable and controllable" working state. This dimension deeply integrates point inspection data, real-time monitoring signals (such as vibration, temperature and pressure) and reliability indicators (such as mean time between failures, mean time to repair and equipment integrity), and realizes early identification of state degradation through setting dynamic threshold. For key components, once the monitoring value exceeds the preset safety interval, the system will automatically trigger the deduction mechanism, and even affect the overall health degree score, thereby embodying the design idea of "one vote veto" important indicators. The state dimension is not only the premise of the performance of other dimensions, but also the main source of input of failure modes in subsequent failure mode and effect analysis, and constitutes the technical bottom layer of health assessment.

[0031] Equipment performance health is a key pillar of the equipment health evaluation system that measures the economic efficiency and resource utilization efficiency. It focuses on the effective output capacity and energy consumption level of the equipment in unit time, directly reflecting whether the equipment is in a state of "high yield, low consumption, and high efficiency". This dimension deeply integrates production efficiency indicators (such as hourly output and unplanned downtime frequency) and energy consumption (such as unit output comprehensive energy consumption and single-machine energy efficiency ratio, including electricity, steam, and compressed air), and through setting dynamic energy efficiency thresholds based on historical benchmarks or industry benchmarks, it realizes early identification of efficiency decline or energy consumption anomalies. When the actual energy efficiency of the equipment deviates significantly from the reasonable range, for example, the unit output power consumption continues to exceed the standard or the effective operation time ratio is too low, the system will automatically trigger the deduction mechanism and weightedly affect the overall health score according to the deviation degree, embodying the evaluation concept of "low efficiency means health damage". The performance dimension not only reveals the running rhythm problem of the equipment itself, but also exposes the coordination short board in production organization, maintenance response, and energy management, which is an important basis for driving lean operation and green manufacturing, and constitutes the value layer of the health assessment from "usable" to "good use and energy saving".

[0032] Product quality health closely binds the equipment health with the final product output of the equipment, embodying the core logic of "equipment health means process stability, and process stability means reliable quality". This dimension mainly relies on online quality detection systems, process parameter records, and other data to quantify the equipment's ability to ensure product quality in the production process. Typical indicators include finished product pass rate, process capability index of key process parameters (such as moisture CPK), and export standard deviation. When the equipment causes quality fluctuations due to wear, misadjustment, or control failure, the quality dimension score will decrease significantly, and potential causes can be traced back to the state or efficiency dimension. This "result-oriented + process tracing" design is a key scale for measuring the overall health level of the equipment.

[0033] Equipment cost health examines the sustainability of equipment operation from an economic perspective, emphasizing that "health does not mean high investment, but high cost performance". This dimension integrates material loss (tobacco crushing rate, auxiliary material waste), equipment maintenance cost (spare parts replacement cost, maintenance cost, and maintenance cost), etc. Different equipment types can be configured with different cost structure weights, such as focusing on energy consumption for power equipment and focusing on auxiliary materials and spare parts for rolling equipment. All cost indicators are benchmarked against historical benchmarks or industry benchmarks, and the greater the deviation, the more points are deducted. More importantly, the cost dimension forms a feedback loop with the state and efficiency dimensions - if a certain equipment causes maintenance cost to soar due to aging but efficiency does not improve, its overall health will be reasonably downgraded, providing data support for updating and upgrading.

[0034] Process management health degree is used to evaluate the advancement, optimization ability and continuous improvement level of production or management process. Healthy equipment cannot be separated from standardized management and complete archives. A complete equipment health record should include maintenance records, spare parts replacement, whether predictive maintenance is carried out, failure analysis, etc.

[0035] The equipment safety health degree, as the bottom line constraint and red line index of the equipment health degree evaluation system, ensures that the equipment operation is always in a controlled state of personnel, environmental and asset safety. This dimension comprehensively covers the elements of personal safety and occupational health, including not only traditional safety control items such as safety interlock effectiveness, protection device integrity, real safety alarm frequency and hidden danger rectification closed loop rate, but also factors affecting human health such as noise level in equipment operation area, dust removal system operation state and dust concentration monitoring data, to ensure compliance with national occupational health and environmental protection regulations. The safety dimension adopts a "one vote veto" mechanism: once the system judges that there is a major safety hazard that has not been rectified (such as safety interlock failure, dust seriously exceeding the standard due to the shutdown of the dust removal system, long-term noise exceeding the limit without effective protection), or the environmental protection compliance is reported by the supervision department, the safety dimension score is immediately zero, and the total health degree of the equipment is forced to limit the upper limit (such as 60 points), until the problem is completely closed. This design makes the health degree evaluation not only focus on "whether the equipment can be used", but also emphasizes "whether it is dare to use and can be used with confidence", and truly puts the safety and health of personnel life in the first place of equipment management.

[0036] Step 2, free configuration index: for different types of equipment (such as wrapping machines, silk making lines, shuttle cars, and moisture removal fans), specific secondary evaluation indexes are configured for the six dimensions through free configuration. For example, for wrapping equipment, indexes related to product quality and material loss are configured, that is, the weight of the P_Quality dimension is configured to be higher than that of other dimensions; while for logistics, power and silk making equipment, indexes related to vibration and temperature monitoring are configured, that is, the weight of the P_Status dimension is configured to be higher than that of other dimensions.

[0037] Specifically, for example, Figure 2As shown, the secondary evaluation indicators configured for P_Status include: vibration monitoring, temperature monitoring, pressure monitoring, overall equipment effectiveness (OEE), mean time between failures (MTBF) / mean time to repair (MTTR); the secondary evaluation indicators configured for P_Efficiency include: hourly output, time utilization rate, input-output ratio, energy consumption per unit output, raw material consumption rate; the secondary evaluation indicators configured for P_Quality include: yield, defect rate, process parameter CPK; the secondary evaluation indicators configured for P_Cost include: unit output maintenance cost, spare parts capital turnover rate; the secondary evaluation indicators configured for P_Innovation include: maintenance plan execution rate, preventive maintenance proportion; the secondary evaluation indicators configured for P_Safety include: equipment integrity, equipment service age, equipment newness coefficient.

[0038] Step 3, dynamic weight distribution: the dynamic weight distribution of the present application adopts a mode of mainly based on FMEA strategy and secondarily based on management strategy for dynamic weight distribution. First, the weight of each dimension is adjusted based on the FMEA strategy, that is, failure mode and effects analysis (FMEA) is performed on the equipment, the risk priority number (RPN) of each failure mode is calculated, and the failure modes are classified into corresponding dimensions, and the dimension weights W1 to W6 in step 1 are dynamically distributed and adjusted according to the total RPN value under each dimension; wherein the higher the total RPN value of a certain dimension, the greater its weight. At the same time, for this dimension, it is associated with specific secondary evaluation indicators, and the internal weights of the corresponding secondary evaluation indicators are dynamically adjusted.

[0039] Failure mode and effects analysis (FMEA) in the field of equipment maintenance usually refers to equipment FMEA or maintainability FMEA. FMEA is a prospective, systematic and team-based risk assessment method, the core of which is "prevention is better than correction", which aims to systematically identify all potential failure modes, the causes and effects of each failure mode, evaluate the risk priority of each failure mode, and develop and implement preventive / detection measures before equipment failure occurs.

[0040] Failure mode and effects analysis includes the following steps and contents:

[0041] 1. Form a cross-functional team: including equipment engineers, maintenance technicians, operators, process engineers, etc.;

[0042] 2. Define the scope of analysis: clearly define the equipment, system or subsystem to be analyzed;

[0043] 3. Identify functions and potential failure modes: list the functions of each component and think of all possible ways to fail to perform that function (e.g. leakage, breakage, blockage, short circuit, wear and tear, etc.);

[0044] 4. Analyze the impact of failure: assess the consequences of the failure on the equipment itself, upstream and downstream processes, product quality, safety, environment, etc.

[0045] 5. Analyze the cause of failure: trace the root cause of the failure (e.g. lack of lubrication, fatigue stress, foreign object intrusion, installation error, etc.);

[0046] 6. Identify existing control measures: list the measures currently taken to prevent the occurrence of the failure or to detect it in a timely manner after its occurrence (e.g. regular point inspection, vibration monitoring, operating procedures, protective devices, etc.);

[0047] 7. Perform risk assessment (calculate RPN);

[0048] 8. Develop and implement improvement measures: for high-risk items, develop new prevention or detection measures and assign a responsible person and a completion deadline;

[0049] 9. Reassess risk: after the implementation of measures, re-score to verify that the risk has been reduced to an acceptable level.

[0050] In addition, regarding the calculation of the risk coefficient (RPN), RPN is used to quantify and rank the risk levels of different failure modes, and the RPN calculation formula is:

[0051] RPN = S x O x D (2)

[0052] Where: S represents severity; O represents occurrence; D represents detection.

[0053] These three scoring factors need to be scored by the FMEA team according to experience, data and judgment within the pre-defined scoring criteria (usually 1-10 points) at the beginning. The details of each factor and the typical scoring criteria are as follows:

[0054] (1) Severity

[0055] Definition: the severity of the impact on the final consequences (e.g. safety, environment, production, quality) if the failure mode occurs.

[0056] Scoring principle: only assess the severity of the consequences, regardless of the frequency of occurrence.

[0057] Typical scoring criteria example:

[0058] 10 points (catastrophic): causes personnel injury, severe damage to equipment, major environmental accidents;

[0059] 7-9 points (severe / high): causes equipment unplanned downtime, loss of major functions, product batch scrap;

[0060] 4-6 points (moderate): cause performance degradation, need to stop maintenance, produce some unqualified products;

[0061] 1-3 points (slight): slight performance impact, can be adjusted by routine maintenance, almost no impact on production.

[0062] (2) Occurrence

[0063] Definition: The likelihood or frequency of the cause of this failure mode occurring.

[0064] Scoring principle: Evaluate the likelihood of the cause occurring, based on historical data or experience estimates.

[0065] Typical scoring standard examples:

[0066] 10 points (extremely high): almost certainly occurs, failure probability > 1 / 2;

[0067] 7-9 points (high): occurs repeatedly, 1 / 20 < failure probability ≤ 1 / 2;

[0068] 4-6 points (moderate): occurs occasionally, 1 / 2000 < failure probability ≤ 1 / 20;

[0069] 1-3 points (low): rarely occurs, failure probability ≤ 1 / 2000.

[0070] (3) Detection

[0071] Definition: The likelihood of discovering the failure cause or mode using existing control measures before the failure mode occurs and causes an impact (or flows into the next process).

[0072] Scoring principle: Evaluate the effectiveness of the detection means, the higher the score the more difficult to detect.

[0073] Typical scoring standard examples:

[0074] 10 points (extremely low): no existing control measures, or measures are extremely unreliable and cannot be detected.

[0075] 7-9 points (low): there is a small probability of discovering through manual inspection or alarms before the failure occurs and causes an impact.

[0076] 4-6 points (moderate): there is a high probability of discovering through regular preventive maintenance (such as monthly point inspection).

[0077] 1-3 points (high): almost certainly discovered immediately through online real-time monitoring (such as vibration sensors, temperature sensors, SCADA systems).

[0078] After calculating the RPN value according to the formula, the enterprise usually sets an action threshold for RPN (such as RPN>100 or S>8), and once it is exceeded, improvement measures must be developed and implemented.

[0079] Then, based on the management strategy, the weight is corrected, that is, in the cost control period, quality improvement month, equipment overhaul, etc., the weight of the corresponding dimension is manually adjusted to fit the current management goal. For example, in the "cost control period", the weight of P_Cost is increased; in the "quality improvement month", the weight of P_Quality is increased; and in the "after equipment overhaul", the weight of P_Status is increased.

[0080] Step 4, multi-source data fusion and normalization: collect and integrate multi-source data from SCADA (real-time data), MES (production data), MRO (maintenance data), and ERP (financial data) systems to obtain specific secondary evaluation indicators for six dimensions. For secondary evaluation indicators of different dimensions, use piecewise linear function, Boolean judgment, etc. to normalize and map them to the score interval of [0, 100]. For the vibration index under the P_Status dimension, strictly follow the standard "GB / T 6075.3-2012 Measurement and Evaluation of Machine Vibration on Non-Rotating Parts" to set threshold and normalization rules. For the secondary evaluation indicators under the P_Status dimension, set a veto rule: when the normalized score of the indicator is lower than the preset critical threshold, directly limit or significantly reduce the health score of the dimension P_Status it belongs to.

[0081] Step 5, calculate the overall health of the equipment: according to the normalized secondary indicator score and its internal weight, the health score of each dimension (including P_Status, P_Efficiency, P_Quality, P_Cost, P_Innovation, and P_Safety) is calculated in turn. Finally, the overall health of the equipment OEH is calculated by weighted summation according to formula (1).

[0082] Then the calculation results are visualized through the health instrument panel, including the overall health OEH and the health score of each dimension; and drilling analysis function is provided, so that users can drill down from the overall health to the specific low-score dimension, and then drill down to the specific abnormal secondary indicator, and view the corresponding abnormal point inspection record or fault work order to quickly locate the root cause.

[0083] Example One

[0084] This embodiment takes a kind of ring-shaped shuttle vehicle (model: RGV-AC10) of cigarette enterprise logistics workshop as an example, in combination with its actual point inspection, FMEA, OEE data, the above-mentioned method is described in detail.The shuttle vehicle is responsible for the transport task of raw material package and finished product box on the ring track, and its health status directly affects the throughput efficiency of the entire logistics system.

[0085] This embodiment discloses a kind of based on multi-dimensional dynamic evaluation of cigarette enterprise equipment health degree calculation method, comprising the following steps:

[0086] Step 1, construct equipment health degree hierarchical evaluation model: first, in the device management background of system, for the ring-shaped shuttle vehicle (equipment code: RGV-AC10-01) create health degree model, i.e.:

[0087] OEH =W1×P_Status +W2×P_Efficiency + W3×P_Quality + W4×P_Cost + W5×P_Innovation + W6×P_Safety (1)

[0088] Step 2, free configuration index: for the equipment of this embodiment, configure specific, quantifiable secondary evaluation index for each dimension, and set its data source and normalization rule.For example:

[0089] For P_Status dimension, the secondary evaluation index configured includes:

[0090] Secondary evaluation index 1: walking wheel vibration value (mm / s), data is derived from vibration acceleration sensor (model: IPC-620M) installed on walking wheel bearing seat, sampling frequency is 10kHz, and effective value is collected and calculated by PLC built-in device.

[0091] Secondary evaluation index 2: power supply current stability, by calculating the variation coefficient (CV) of motor driving current collected by current transmitter in a single task cycle.

[0092] Secondary evaluation index 3: communication interruption rate (%), by upper layer scheduling system (WMS) by counting the number of MODBUS-TCP communication timeout with shuttle vehicle PLC every 15 minutes.

[0093] Corresponding P_Quality dimension, the secondary evaluation index configured includes:

[0094] Secondary index 1: task accuracy rate (%), verified by barcode scanning system and WMS, and the calculation formula is:

[0095] (1 - Misplacement frequency / Total task execution number) * 100%.

[0096] Step 3, Dynamic Weight Distribution: When the system is initialized, all dimensions and secondary indicators use default weights (such as average weight). After the system runs for a month, the weight dynamic adjustment based on FMEA strategy is started.

[0097] FMEA Analysis Integration: Maintenance engineers add a fault mode record in the FMEA module of the system for RGV-AC10:

[0098] Fault Mode: Intermittent interruption of MODBUS network communication;

[0099] Severity (S): 8 (causing shuttle car to stop suddenly, blocking the entire line);

[0100] Frequency (O): 6 (4 times in the past month);

[0101] Detection (D): 7 (fault point difficult to quickly locate);

[0102] Calculate RPN: 8 * 6 * 7 = 336;

[0103] Associated Dimensions: Engineers associate this fault mode to the "communication interruption rate" indicator under the P_Status dimension.

[0104] Weight Adjustment Execution: The system recognizes that this RPN value is the highest among all fault modes in the current period. According to the preset rules (for example, the dimension weight of the highest RPN item increases by 10%), the system automatically increases the P_Status dimension weight W1 from the initial 0.14 to 0.154. At the same time, within the P_Status dimension, the internal weight of the communication interruption rate secondary indicator is increased by 20%.

[0105] Step 4, Multi-source data fusion and normalization: Take "walking wheel vibration value" as an example to explain the conversion process from raw data to standardized score.

[0106] Data Collection: Sensor reports a valid vibration value data: 3.3 mm / s.

[0107] Normalization Rule Invocation: The system queries the preset piecewise linear normalization function for this indicator. The rule is defined as:

[0108] (1) Vibration value ≤ 1.8 mm / s, score = 100 points (excellent state);

[0109] (2) 1.8 mm / s < vibration value ≤ 2.8 mm / s, score = 100 - [(measured value - 1.8) / (2.8 - 1.8)] * 20 (linear deduction, allowed state);

[0110] (3) vibration value > 2.8 mm / s, score = 100 - [(measured value - 1.8) / (2.8 - 1.8)] * 20 (linear deduction, <80 points below for state degradation).

[0111] Score calculation: substitute the measured value 3.3 into the formula: score = 100 - [(3.3 - 1.8) / 1.0] * 20 = 100 - 31 = 69 points.

[0112] Therefore, the normalized score of this data point is 69 points.

[0113] Step 5, calculate the overall health of the device: at the end of an evaluation period (e.g. every Sunday night 12pm), the system automatically performs the calculation. The health visualization dashboard and drill-down analysis interface of this embodiment are as shown in Figure 3 .

[0114] Dimension score calculation:

[0115] P_Status dimension score = (walking wheel vibration value score * its weight + power supply current stability score * its weight + communication interruption rate score * its weight) = (69 * 0.4 + 88 * 0.3 + 70 * 0.3) = 75 points.

[0116] P_Quality dimension score = task accuracy rate score = 98 points.

[0117] ... (other dimensions are calculated similarly)

[0118] In this embodiment, W1 corresponds to P_Status, W2 corresponds to P_Efficiency, W3 corresponds to P_Quality, W4 corresponds to P_Cost, W5 corresponds to P_Innovation, and W6 corresponds to P_Safety. In practice, the order of each dimension can be adjusted, and even some dimensions have a weight of 0.

[0119] Assume complete weights: W1=0.154, W2=0.146, W3=0.14, W4=0.26, W5=0.15, W6=0.15 (sum = 1);

[0120] Thus, OEH = 75x0.154 + P_Efficiency Score x 0.146 + 98x0.14 + P_Cost Score x 0.26 + P_Innovation Score x 0.15 + P_Safety Score x 0.15 = 88 points.

[0121] Visualization: The system renders an automobile dashboard-style OEH indicator for this shuttle vehicle on the digital dashboard of the plant's monitoring wall. The needle points to 88, while the background color shows amber, representing "Good". The dashboard also displays the OEH trend of this device over the past 12 weeks in the form of a trend curve.

[0122] Trend alert: The plant manager notices that the OEH trend of RGV-AC10 has been continuously dropping from 92 → 90 → 88 in the last three weeks. He clicks on the OEH icon of this shuttle vehicle on the dashboard to initiate a "drill-down analysis".

[0123] Root cause localization:

[0124] First-level drill-down: The system displays the score changes of each dimension and finds that the drop of P_Status from 92 to 75 is the main cause.

[0125] Second-level drill-down: By clicking on the P_Status dimension, the system displays the historical data of its subordinate secondary indicators and finds that the weekly average score of "Walking Wheel Vibration Value" has rapidly dropped from 99 to 69 (corresponding to the vibration value rising from 1.85 mm / s to 3.3 mm / s).

[0126] Intelligent correlation and decision suggestion:

[0127] The system automatically correlates with the electronic point inspection system and retrieves the historical point inspection records of this walking wheel, finding that the last "Wheel Rim Wear Inspection" record (3 weeks ago) is "Minor wear, continue to observe".

[0128] Based on the correlation analysis of "vibration value rising" and "point inspection record", the system automatically pops up a red alert box on the drill-down analysis page, saying: "Detected rising trend of walking wheel vibration value. Combined with historical point inspection records, it is inferred that there is an accelerated wear risk of the walking wheel. It is recommended to arrange a shutdown inspection within 48 hours."

[0129] Work order generation: After the maintenance foreman confirms the suggestion, he generates a "predictive maintenance work order" in the integrated computerized maintenance management system (CMMS) with one click. The work order type is automatically identified as "predictive maintenance" and is associated with relevant fault analysis and handling suggestions. The maintenance team performs the inspection according to the plan during the planned downtime window, and indeed finds that the walking wheel is worn out and is replaced in time, avoiding a non-planned downtime (estimated to avoid 4 hours of downtime) caused by the walking wheel jamming.

[0130] This embodiment fully demonstrates the closed-loop process of the application from data collection, model calculation, dynamic adjustment to visualization, root cause analysis and decision support from the technical detail level, which proves that the application scheme is not only a theoretical model, but also a systematic engineering solution that can be implemented and effectively improve the intelligent level of equipment management and decision-making efficiency.

[0131] Finally, it should be noted that the above description is only to illustrate the technical solutions of the application and is not limiting. Although the application has been described in detail with reference to the preferred arrangement, it should be understood by those skilled in the art that the technical solutions of the application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the application.

Claims

1. A method for calculating the health status of equipment in tobacco manufacturing enterprises based on multi-dimensional dynamic assessment, characterized in that, The method includes the following steps: Step 1: Construct a hierarchical assessment model for equipment health: A hierarchical assessment model for the equipment health of cigarette manufacturing enterprises is constructed. This model defines the overall equipment health as a weighted sum of six dimensions: equipment operational health, equipment efficiency health, product quality health, equipment cost health, process management health, and equipment safety health. The model expression is as follows: OEH =W1×P_Status +W2×P_Efficiency + W3×P_Quality + W4×P_Cost + W5×P_Innovation + W6×P_Safety (1) Wherein, OEH represents the overall health of the equipment; P_Status represents the health of the equipment's operational status; P_Efficiency represents the health of the equipment's efficiency; P_Quality represents the health of the product's quality; P_Cost represents the health of the equipment's cost; P_Innovation represents the health of the process management; P_Safety represents the health of the equipment's safety; W1~W6 are the weights of each dimension, and W1 + W2 + ... + W6 = 1; Step 2: Configure indicators freely: For different types of equipment, configure specific secondary evaluation indicators for the six dimensions through free configuration. Step 3, Dynamic Weight Allocation: First, adjust the weights of each dimension based on the FMEA strategy, that is, perform Failure Mode and Effects Analysis on the equipment, calculate the risk coefficient (RPN) of each failure mode, and classify the failure modes into the corresponding dimensions. Dynamically allocate and adjust the weights W1 to W6 of each dimension in Step 1 based on the total RPN value under each dimension; then, correct the weights based on the management strategy. Step 4: Multi-source data fusion and normalization: Collect and integrate multi-source data from SCADA (real-time data), MES (production data), MRO (maintenance data), and ERP (financial data) systems to obtain specific secondary evaluation indicators for six dimensions; for secondary evaluation indicators with different dimensions, perform normalization processing and uniformly map them to the score range of [0, 100]. Step 5: Calculate the overall health of the equipment: Based on the normalized secondary index scores and their internal weights, calculate the health scores of each dimension in turn; then calculate the overall health of the equipment, namely OEH, using formula (1).

2. The method for calculating the health status of cigarette manufacturing equipment based on multi-dimensional dynamic evaluation according to claim 1, characterized in that, Step 2 includes the following secondary evaluation indicators for equipment operating health: vibration monitoring, temperature monitoring, pressure monitoring, overall equipment efficiency, and mean time between failures / mean time to repair. For equipment performance health, the secondary evaluation indicators include: hourly output, time utilization rate, input-output ratio, energy consumption per unit output, and raw material consumption rate. For product quality health, the secondary evaluation indicators include: yield rate, defect rate, and process parameter CPK. For equipment cost health, the secondary evaluation indicators include: maintenance cost per unit output and spare parts capital turnover rate. For process management health, the secondary evaluation indicators include: maintenance plan execution rate and preventative maintenance ratio. For equipment safety health, the secondary evaluation indicators include: equipment integrity, equipment service life, and equipment newness coefficient.

3. The method for calculating the health status of cigarette manufacturing equipment based on multi-dimensional dynamic evaluation according to claim 1, characterized in that, The formula for calculating the risk factor (RPN) for each failure mode in step 3 is as follows: RPN=S×O×D (2) Where: S represents severity; O represents occurrence; D represents detectability.

4. The method for calculating the health status of cigarette manufacturing equipment based on multi-dimensional dynamic evaluation according to claim 1, characterized in that, In step 3, when dynamically allocating and adjusting the weights W1 to W6 of each dimension in step 1 based on the total RPN value of each dimension, the higher the total RPN value of a certain dimension, the greater its weight. At the same time, for that dimension, it is associated with its specific secondary evaluation indicators, and the internal weights of the corresponding secondary evaluation indicators are dynamically adjusted.

5. The method for calculating the health status of cigarette manufacturing equipment based on multi-dimensional dynamic evaluation according to claim 1, characterized in that, Step 3 involves adjusting the weights based on management strategies. Specifically, during cost control periods, quality improvement months, and after major equipment overhauls, the weights of the corresponding dimensions are manually adjusted to align with current management objectives.

6. The method for calculating the health status of cigarette manufacturing equipment based on multi-dimensional dynamic evaluation according to claim 1, characterized in that, In step 5, after calculating the overall health of the equipment, the calculation results are visualized through the health dashboard; and a drill-down analysis function is provided, which allows users to drill down from the overall health to specific low-score dimensions, and then to specific abnormal secondary indicators, and to view the corresponding abnormal inspection records or fault work orders to locate the cause of the fault.

Images