Maintenance decision-making method for energy consumption optimization of green manufacturing system

Abstract

The invention relates to a maintenance decision-making method oriented to energy consumption optimization of a green manufacturing system in the field of maintenance decision-making, in particular to a multi-level maintenance decision-making method oriented to an energy consumption optimization window for a complex manufacturing system composed of a plurality of devices. In the equipment layer, the influence of the degradation process, the actual maintenance capability, the actual service environment and other conditions of each equipment is comprehensively considered, and a multi-target preventive maintenance decision model covering the minimum maintenance cost, the maximum equipment availability and the minimum system energy consumption is formed through overall planning; and dynamic output of preventive maintenance time points is realized for each device in the green manufacturing system. In the system layer, a downtime interval caused by order switching of the green manufacturing system is used as an energy consumption optimization window, the energy consumption association relationship among multiple devices and the mutual influence degree of production batches are analyzed, an energy consumption quantification criterion capable of being evaluated in real time is formed, and whether the devices are subjected to combined maintenance or not is judged. The maintenance decision complexity of the green manufacturing system is greatly reduced, and the energy consumption optimization efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to a technology in the field of maintenance decision-making, in particular to a maintenance decision-making method for optimizing energy consumption of a green manufacturing system. Background Art

[0002] Manufacturing systems need to balance production efficiency and energy consumption when transforming to green manufacturing. Traditional manufacturing system maintenance decision-making methods often take equipment reliability and production efficiency as the main considerations, ignoring energy consumption optimization and environmental impact. It is urgent to establish a maintenance decision-making method for energy consumption optimization of complex manufacturing systems with multiple devices. Summary of the invention

[0003] To solve the above problems, the present invention provides a maintenance decision method for optimizing energy consumption in green manufacturing systems. Aiming at a complex manufacturing system composed of multiple devices, a multi-level maintenance decision method guided by an energy consumption optimization window is designed. At the equipment level, the degradation process, actual maintenance capability, and actual service environment of each device are comprehensively considered to form a multi-objective preventive maintenance decision model covering maintenance cost minimization, equipment availability maximization, and system energy consumption minimization, and the dynamic output of preventive maintenance time points is realized for each device in the green manufacturing system; at the system level, the downtime interval caused by order switching in the green manufacturing system is used as an energy consumption optimization window, and the energy consumption correlation relationship between multiple devices and the degree of mutual influence of production batches are analyzed to form a quantitative energy consumption criterion that can be evaluated in real time and determine whether to perform combined maintenance on each device, which greatly reduces the complexity of maintenance decision-making in the green manufacturing system and significantly improves the efficiency of energy consumption optimization.

[0004] The present invention is achieved through the following technical solutions:

[0005] The present invention relates to a maintenance decision method for energy consumption optimization of green manufacturing systems. Aiming at the urgent demand for energy consumption optimization of green manufacturing systems, the method carries out multi-device maintenance decision-making oriented to energy consumption minimization for series-connected devices in a batch production process. The method mainly comprises a model building module, a dynamic adjustment module and a continuous updating module. The model building module refers to two main steps of outputting three types of preventive maintenance time points and establishing a multi-objective optimal preventive maintenance model. The dynamic adjustment module outputs an opportunistic maintenance combination plan for an order conversion interval. The continuous updating module performs combined maintenance with energy consumption minimization and continuous updating of the maintenance plan according to the real-time monitoring information of the equipment and the energy consumption optimization results until the system stops running.

[0006] The model building module first plans and outputs three types of preventive maintenance time points for each device, combines the three types of preventive maintenance time points to establish a multi-objective optimal preventive maintenance model, and periodically outputs the preventive maintenance time points for each device; specifically includes:

[0007] Step 1: According to the failure rate distribution function of each device, the energy consumption demand, cost demand and equipment availability demand of the green manufacturing system are analyzed, and the preventive maintenance time point of each device is calculated respectively through the energy consumption rate model, cost rate model and availability model;

[0008] Step 2: Integrate the maintenance energy consumption rate model, cost rate model and availability model to establish a multi-objective optimal preventive maintenance model, unify the unit dimensions and gradient optimization directions of the three types of model objectives, and progressively output the optimal preventive maintenance time point for each equipment;

[0009] The three types of preventive maintenance time points are the preventive maintenance time point when the energy consumption output is minimum, the preventive maintenance time point when the maintenance cost is minimum, and the preventive maintenance time point when the availability is minimum.

[0010] The dynamic adjustment module refers to utilizing the production conversion opportunities of each device in the green manufacturing system, taking the low-power standby interval as a combination maintenance opportunity, establishing a correlation between the random order production plan and the energy consumption in the green manufacturing system structure, and dynamically adjusting the opportunity maintenance combination plan of each device in the order conversion interval in the green manufacturing system; specifically including:

[0011] Step a: Consider the evolution of the failure rate of each device in the green manufacturing system during different maintenance cycles, and progressively output the failure rate function of each device after different maintenance times;

[0012] Step b, finding each order conversion interval in the green manufacturing system, and establishing a combination maintenance empty set of the current most recent order conversion interval;

[0013] Step c, quantify the energy consumption of each equipment maintenance operation in advance to the order conversion interval, specifically: including the change in downtime energy consumption saved by the equipment to be repaired and the change in standby energy consumption saved by the non-repair equipment after the maintenance operation is advanced to the order conversion interval; the change in energy consumption increased by the subsequent maintenance operation of the equipment to be repaired due to the advance maintenance operation; the change in energy consumption reduced by the minor repair operation of the equipment to be repaired due to the advance maintenance operation;

[0014] Step d, quantify the energy consumption of each equipment after the maintenance operation is postponed to the order conversion interval, specifically: including the change in downtime energy consumption saved by the equipment to be repaired and the change in standby energy consumption saved by the non-repair equipment after the maintenance operation is postponed to the order conversion interval; the change in energy consumption reduced by the subsequent maintenance operation of the equipment to be repaired due to the postponement of the maintenance operation; the change in energy consumption increased by the minor repair operation of the equipment to be repaired due to the postponement of the maintenance operation;

[0015] Step e, taking the current order conversion interval as the energy consumption optimization window, and performing real-time calculation of the energy consumption change amount for whether the maintenance operation of each device is adjusted to the energy consumption optimization window, specifically: if the total amount of energy consumption change of the maintenance operation of a single device in the energy consumption optimization window in advance and the total amount of energy consumption change of the maintenance operation after the maintenance operation are both less than 0, no adjustment is made, and the equipment is maintained at the original time; if the total amount of energy consumption change of the maintenance operation of a single device in the energy consumption optimization window in advance is greater than 0 and the total amount of energy consumption change of the maintenance operation after the maintenance operation is less than 0, the maintenance operation is adjusted in advance; if the total amount of energy consumption change of the maintenance operation of a single device in the energy consumption optimization window in advance is less than 0 and the total amount of energy consumption change of the maintenance operation after the maintenance operation is greater than 0, the maintenance operation is adjusted to be delayed; if the total amount of energy consumption change of the maintenance operation of a single device in the energy consumption optimization window in advance and the total amount of energy consumption change of the maintenance operation after the maintenance operation are both greater than 0, the maintenance operation is adjusted in advance or delayed according to the maximum value of the total amount of energy consumption change of the maintenance operation in advance and the total amount of energy consumption change of the maintenance operation after the maintenance operation is delayed.

[0016] The continuous updating module refers to: updating the actual maintenance time points of each current device according to the actual order conversion interval and maintenance decision, and continuing to cyclically execute the combined maintenance decision optimization of the next maintenance cycle. Technical Effects

[0017] The maintenance decision method for optimizing energy consumption of green manufacturing systems proposed in the present invention can comprehensively consider factors such as the degradation process of each equipment, the actual maintenance capability and the service environment, construct a multi-objective preventive maintenance decision model, and model the preventive maintenance time points of each equipment under the conditions of minimizing maintenance cost, maximizing equipment availability and minimizing system energy consumption. It can analyze the energy consumption correlation between multiple equipment and the mutual influence of production batches in real time, and optimize the judgment whether to perform combined maintenance on each equipment. Compared with the existing workshop production and maintenance technology, the present invention can dynamically and cyclically provide a multi-machine collaborative maintenance scheduling solution with linear speed solution and reduced energy consumption for the green manufacturing system, and provides an effective method for overcoming the challenge of the explosion of decision complexity exponent in traditional opportunistic maintenance strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a maintenance decision method for optimizing energy consumption of a green manufacturing system proposed in the present invention. DETAILED DESCRIPTION

[0019] The maintenance decision method for optimizing energy consumption of a green manufacturing system proposed in this embodiment includes the following implementation steps:

[0020] Step 1: Based on the failure rate distribution function of each device ,in Number the preventive maintenance cycles of the green manufacturing system. Number the devices, starting from the first device layer maintenance cycle First, the energy consumption demand, cost demand and equipment availability demand of the green manufacturing system are analyzed through the energy consumption rate model, cost rate model and availability model; among them, the energy consumption rate model expression is The cost rate model expression is: , the availability model expression is ;in, Represents the cost of preventive maintenance operations, Represents the cost of minor repairs, Represents the power of preventive maintenance operations, Represents the power of minor repair work, Represents the duration of preventive maintenance work Represents the duration of minor repair work and calculates the preventive maintenance time of each equipment separately .

[0021] Step 2: Integrate the maintenance energy consumption rate model , cost rate model Availability Model , establish a multi-objective optimal predictive maintenance model ,in , and are the weights of the energy consumption rate model, cost rate model and availability model respectively, where ,, unify the unit dimensions and gradient optimization directions of the three types of model objectives, and progressively output the optimal preventive maintenance time point for each equipment .

[0022] Step 3: Consider the evolution of the failure rate of each device in the green manufacturing system during different maintenance cycles, and progressively output the failure rate function of each device after different maintenance times. The expression is: ,in Represents the interval between two consecutive maintenance decisions made by each device at the system level. ( ) is the residual factor of service age, ( ) is the failure rate acceleration factor, ( ) are environmental impact factors.

[0023] Step 4: Find the order conversion intervals in the green manufacturing system [t B k , t B k+1 ] , Representative order batches, establish the combination of the current and recent order conversion intervals and maintain an empty set , from Start the cycle, based on the preventive maintenance point input at the equipment level , determine whether to include each device in the combined maintenance empty set , and expressed as Φ(j, t Bk )= 0 t ij ∉ ( t Bk , t Bk + T B(k + 1) ] 1 t ij ∈ ( t Bk , t Bk + T B(k + 1) ] .

[0024] Step 5: Right The energy consumption of each equipment maintenance operation is quantified in advance to the order conversion interval, which is expressed as Specifically: the change in downtime energy consumption saved by the equipment to be repaired after the maintenance work is advanced to the order conversion interval The change in standby energy consumption saved by non-repaired equipment ,in Indicates Standby power of each device, Expressed as The working power of each device, that is, the change in total shutdown energy consumption is ; The change in energy consumption caused by the subsequent maintenance of the equipment to be repaired due to the early maintenance work ; Change in energy consumption due to reduced minor repairs of equipment to be repaired due to early maintenance work .

[0025] Step 6: Right The maintenance work of each equipment is postponed to the order conversion interval to quantify the energy consumption, which is expressed as Specifically, after the maintenance work is postponed to the order conversion interval, the sum of the change in shutdown energy consumption saved by the equipment to be repaired and the change in standby energy consumption saved by the equipment not to be repaired is ; The change in energy consumption due to the subsequent maintenance work of the equipment to be repaired is reduced due to the delay of maintenance work ; Change in energy consumption due to delayed maintenance work on equipment to be repaired .

[0026] Step 7: Take the current order conversion interval as the energy consumption optimization window, and calculate the energy consumption change in real time to see whether the maintenance operation of each device is adjusted to the energy consumption optimization window. Specifically: if the total energy consumption change of the maintenance operation of a single device in the energy consumption optimization window in advance and the total energy consumption change of the maintenance operation after the maintenance operation are both less than 0, no adjustment is made, and the equipment is maintained at the original time; if the total energy consumption change of the maintenance operation of a single device in the energy consumption optimization window in advance is greater than 0 and the total energy consumption change of the maintenance operation after the maintenance operation is less than 0, the maintenance operation is adjusted in advance; if the total energy consumption change of the maintenance operation of a single device in the energy consumption optimization window in advance is less than 0 and the total energy consumption change of the maintenance operation after the maintenance operation is greater than 0, the maintenance operation is adjusted in delay; if the total energy consumption change of the maintenance operation of a single device in the energy consumption optimization window in advance and the total energy consumption change of the maintenance operation after the maintenance operation are both greater than 0, the maintenance operation is adjusted in advance or delayed according to the maximum value of the total energy consumption change of the maintenance operation in advance and the total energy consumption change of the maintenance operation after the maintenance operation is delayed, that is: if and , only in Only real-time predictive maintenance is performed at all times; if and , bring forward maintenance work to time; if and , postpone maintenance work time; if and , select the best predictive maintenance, that is If the maintenance work is done in advance, Select to defer the maintenance job.

[0027] Step 8: Update the actual maintenance time of each device according to the actual order conversion interval and maintenance decision. , Based on the system-level maintenance scheduling feedback and equipment-level maintenance planning output, Expressed as: , update the new predicted maintenance time of each device, including Indicates that in the optimal combination set Is there any equipment that needs preventive maintenance? If a new batch of orders arrives, return to step 4 to re-judge , in order to continue to cyclically execute the combined maintenance decision optimization of the next maintenance cycle and realize the energy consumption-oriented multi-machine maintenance decision of the green manufacturing system.

[0028] Table 1 provides the preventive maintenance scheduling scheme for each combined maintenance set of the entire green manufacturing system.

[0029] Table 1 Preventive maintenance scheduling schemes for each combination maintenance set of the green manufacturing system

[0030] The system front-end display software is PYQT5, and the background planning software is MATLAB.

[0031] The above-mentioned specific implementation can be partially adjusted in different ways by those skilled in the art without departing from the principle and purpose of the present invention. The protection scope of the present invention shall be based on the claims and shall not be limited by the above-mentioned specific implementation. Each implementation scheme within its scope shall be subject to the constraints of the present invention.

Claims

1. A maintenance decision method for optimizing energy consumption of green manufacturing system, characterized in that: In response to the urgent need for energy consumption optimization of green manufacturing systems, multi-equipment maintenance decisions oriented towards energy consumption minimization are carried out for series equipment in mass production processes; it mainly includes model building module, dynamic adjustment module and continuous update module; the model building module refers to the two main steps of outputting three types of preventive maintenance time points and establishing a multi-objective optimal preventive maintenance model; the dynamic adjustment module outputs the opportunistic maintenance combination plan for the order conversion interval; the continuous update module performs combined maintenance with energy consumption minimization and continuous update of the maintenance plan based on the real-time monitoring information of the equipment and the energy consumption optimization results until the system ends its operation.

2. The maintenance decision method for optimizing energy consumption of green manufacturing system according to claim 1 is characterized in that: The model building module first plans and outputs three types of preventive maintenance time points for each device, combines the three types of preventive maintenance time points to establish a multi-objective optimal preventive maintenance model, and periodically outputs the preventive maintenance time points for each device; specifically includes: Step 1: According to the failure rate distribution function of each device, the energy consumption demand, cost demand and equipment availability demand of the green manufacturing system are analyzed, and the preventive maintenance time point of each device is calculated respectively through the energy consumption rate model, cost rate model and availability model; Step 2: Integrate the maintenance energy consumption rate model, cost rate model and availability model to establish a multi-objective optimal preventive maintenance model, unify the unit dimensions and gradient optimization directions of the three types of model objectives, and progressively output the optimal preventive maintenance time point for each equipment; The three types of preventive maintenance time points are the preventive maintenance time point when the energy consumption output is minimum, the preventive maintenance time point when the maintenance cost is minimum, and the preventive maintenance time point when the availability is minimum.

3. The maintenance decision method for optimizing energy consumption of green manufacturing system according to claim 1 is characterized in that: The dynamic adjustment module refers to utilizing the production conversion opportunities of each device in the green manufacturing system, taking the low-power standby interval as a combination maintenance opportunity, establishing a correlation between the random order production plan and the energy consumption in the green manufacturing system structure, and dynamically adjusting the opportunity maintenance combination plan of each device in the order conversion interval of the green manufacturing system; Specifically include: Step a: Consider the evolution of the failure rate of each device in the green manufacturing system during different maintenance cycles, and progressively output the failure rate function of each device after different maintenance times; Step b, finding each order conversion interval in the green manufacturing system, and establishing a combination maintenance empty set of the current most recent order conversion interval; Step c, quantify the energy consumption of each equipment maintenance operation in advance to the order conversion interval, specifically: including the change in downtime energy consumption saved by the equipment to be repaired and the change in standby energy consumption saved by the non-repair equipment after the maintenance operation is advanced to the order conversion interval; the change in energy consumption increased by the subsequent maintenance operation of the equipment to be repaired due to the advance maintenance operation; the change in energy consumption reduced by the minor repair operation of the equipment to be repaired due to the advance maintenance operation; Step d, quantify the energy consumption of each equipment after the maintenance operation is postponed to the order conversion interval, specifically: including the change in downtime energy consumption saved by the equipment to be repaired and the change in standby energy consumption saved by the non-repair equipment after the maintenance operation is postponed to the order conversion interval; the change in energy consumption reduced by the subsequent maintenance operation of the equipment to be repaired due to the postponement of the maintenance operation; the change in energy consumption increased by the minor repair operation of the equipment to be repaired due to the postponement of the maintenance operation; Step e, taking the current order conversion interval as the energy consumption optimization window, and performing real-time calculation of the energy consumption change amount for whether the maintenance operation of each device is adjusted to the energy consumption optimization window, specifically: if the total amount of energy consumption change of the maintenance operation of a single device in the energy consumption optimization window in advance and the total amount of energy consumption change of the maintenance operation after the maintenance operation are both less than 0, no adjustment is made, and the equipment is maintained at the original time; if the total amount of energy consumption change of the maintenance operation of a single device in the energy consumption optimization window in advance is greater than 0 and the total amount of energy consumption change of the maintenance operation after the maintenance operation is less than 0, the maintenance operation is adjusted in advance; if the total amount of energy consumption change of the maintenance operation of a single device in the energy consumption optimization window in advance is less than 0 and the total amount of energy consumption change of the maintenance operation after the maintenance operation is greater than 0, the maintenance operation is adjusted to be delayed; if the total amount of energy consumption change of the maintenance operation of a single device in the energy consumption optimization window in advance and the total amount of energy consumption change of the maintenance operation after the maintenance operation are both greater than 0, the maintenance operation is adjusted in advance or delayed according to the maximum value of the total amount of energy consumption change of the maintenance operation in advance and the total amount of energy consumption change of the maintenance operation after the maintenance operation is delayed.

4. The maintenance decision method for optimizing energy consumption of green manufacturing system according to claim 1 is characterized in that: The continuous updating module refers to: updating the actual maintenance time points of each current device according to the actual order conversion interval and maintenance decision, and continuing to cyclically execute the combined maintenance decision optimization of the next maintenance cycle.

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