Potential energy recovery and fault maintenance platform for ocean engineering equipment

Abstract

The invention discloses a potential energy recovery and fault maintenance platform for ocean engineering equipment, particularly relates to the technical field of ship data management, and is used for solving the problems that the ocean engineering equipment is high in energy consumption, poor in carbon emission condition and limited in operation cost, and an existing brake resistor system wastes potential energy and is high in fault rate. The system comprises an execution unit, an energy storage unit, a central processing unit, a man-machine interaction unit, a power supply busbar and a data acquisition unit, and the method comprises the following steps: removing a brake resistor; real-time monitoring and optimization decision making are carried out on the state of the three-phase alternating current motor based on dynamic programming and a Markov chain model, and the maintenance period is dynamically adjusted based on a global optimal maintenance strategy; by removing a brake resistor and introducing an energy storage system and a motor intelligent management mechanism, efficient energy utilization, dynamic maintenance optimization, failure rate reduction and carbon emission reduction are realized, and the management participation degree is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ship data management, and more specifically, to a potential energy recovery and fault maintenance platform for marine engineering equipment. Background Art

[0002] The operating efficiency of marine engineering equipment is constrained, the operating cost is high, and the operating safety needs to be improved. The high operating cost is reflected in the limited fossil energy that ships can carry, the inability to use shore power or other non-ship generators, and the inability to maximize the use of available resources and energy. While increasing energy consumption and equipment operating costs, it also increases carbon emissions. As an indispensable engineering equipment for marine engineering ships, the operating level, operating efficiency, reliability and safety of lifting equipment are directly related to whether the engineering ship can complete the engineering task on time, with quality and quantity guaranteed. Traditional lifting equipment is equipped with brake resistors to absorb and consume the energy generated during the lowering of heavy objects to prevent excess energy from causing motor overload and failure. However, brake resistors are purely energy-consuming systems and cannot achieve gravity potential. It can be used by other equipment, resulting in energy waste and increasing the operating cost of the ship. At the same time, the high failure rate of the brake resistor seriously affects the normal use of the lifting equipment, affects the operating efficiency, and delays the completion time. On the other hand, the brake resistor also needs a matching cooling system, which increases the operating cost and brings more hidden dangers to the stable, reliable and safe operation of the ship. In order to protect its own equipment, the marine engineering equipment is equipped with a fixed system that cannot adjust the parameters at will. Users cannot optimize the configuration of parameters according to the actual status of the equipment, and users participate less in management. The emergency response measures are limited, especially for the three-phase AC motor. When there is a fault, it is impossible to guide the selection of the maintenance decision plan based on the current fault status and maintenance decision cost. The Markov chain model based on finite space is a mathematical model used to describe the process of arbitrary transfer between states in a finite state, and each transfer depends only on the current state. How to introduce the Markov chain model to solve the above technical problems has become a problem that needs to be solved urgently. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a potential energy recovery and fault maintenance platform for marine engineering equipment. By removing the brake resistor, introducing the energy storage system and the motor intelligent management mechanism based on dynamic programming and Markov chain model, efficient energy utilization, dynamic maintenance optimization, reduced failure rate and reduced carbon emissions are achieved, and the system safety, reliability and management participation are improved.

[0004] In the present technical solution, when the heavy object is lowered or dragged by other systems, the lifting equipment is equipped with a three-phase AC motor. When the heavy object is lowered, the three-phase AC motor rotates in the opposite direction under the push of the heavy object and is in a generating braking state. The generated AC power is converted into DC via the inverter, and then transmitted to the power supply busbar via the 1# fuse of the execution unit, and then transmitted to the energy storage battery via the 2# fuse in the energy storage system for storage, so as to be used for the operation of other energy-consuming equipment. The system potential energy stored in the energy storage battery transmits the stored DC power in the energy storage unit to the power supply busbar via the 2# fuse of the energy storage unit, and then transmitted to the inverter via the 1# fuse of the execution unit, thereby driving the three-phase AC motor of the terminal actuator, which can reduce the use of fossil energy and operating costs, improve energy utilization, and reduce carbon emissions.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A potential energy recovery and fault maintenance platform for marine engineering equipment, comprising a lifting device, a three-phase AC motor, a frequency converter and a brake resistor connected in sequence, wherein the brake resistor is removed, the frequency converter is connected to a power supply busbar through a 1# fuse, the three-phase AC motor, the frequency converter and the 1# fuse form an execution unit, the power supply busbar is connected to an isolating switch through a 2# fuse, the isolating switch is connected to a battery management system, the battery management system is connected to an energy storage battery, the 2# fuse, the isolating switch, the battery management system and the energy storage battery form an energy storage unit, the battery management system and the execution unit are both connected to a central processing unit unit, the central processing unit unit is connected to a human-computer interaction unit, the battery management system is connected to an energy storage data acquisition module through a bus communication module, the central processing unit is connected to an energy management data acquisition module through a bus communication module, the power supply busbar is connected to a power supply busbar data acquisition module, the energy storage data acquisition module, the energy management data acquisition module and the power supply busbar data acquisition module are connected to the power supply busbar data acquisition module, the energy storage data acquisition module, the energy management data acquisition module and the power supply busbar data acquisition module. The module consists of a data acquisition unit. The central processing unit collects the parameters of the three-phase AC motor to form a time series data set. According to the characteristics of the data, the three-phase AC motor is divided into several states. The transfer frequency between each state is counted as the transfer probability between states. The Markov chain model is used to model the transition between states. The transfer probability of each state is matrixed to form a state transfer matrix. The cost function is set to protect the sum of maintenance cost, downtime cost and failure cost. Through the state transfer matrix and the cost function, a dynamic programming model is constructed. According to the current state of the three-phase AC motor and the state transfer matrix, the dynamic programming recursive formula is used to obtain the expected cost of the next state, traverse the transfer path of each state, and dynamically select the maintenance cycle with the minimum total cost. The human-computer interaction unit displays the current state of the three-phase AC motor, updates the maintenance suggestions in real time based on the dynamic programming results, and displays the cost function value of each maintenance decision and the data of each cost.

[0007] As a further solution of the present invention, the three-phase AC motor parameters collected by the central processing unit include the current, temperature, operating time, historical fault types and historical frequency of occurrence of each type of fault of the three-phase AC motor, and the status of the three-phase AC motor includes normal state, slightly degraded state, moderately degraded state and severely degraded state.

[0008] As a further solution of the present invention, in the central processing unit, the state identification method of the three-phase AC motor is specifically:

[0009] Normal state: The current of the three-phase AC motor remains below 70% of the rated current, the temperature is between 0℃ and 70℃, the cumulative running time does not reach 80% of the preset mid-term maintenance time, and the frequency of minor faults is less than 5 times / 100 hours;

[0010] Mild degradation state: The current of the three-phase AC motor reaches 70% to 85% of the rated current, the temperature is between 70℃ and 80℃, the cumulative running time exceeds 80% of the preset mid-term maintenance time and has not reached the preset mid-term maintenance time, there are no major faults, and the frequency of minor faults is greater than or equal to 5 times and less than 20 times;

[0011] Moderate degradation state: The current of the three-phase AC motor reaches 85% to 95% of the rated current, the temperature is between 80°C and 90°C, and the number of times the temperature reaches 90% of the temperature alarm threshold is more than 3 times but less than 10 times, the cumulative running time exceeds 80% of the preset mid-term maintenance time and is less than or equal to the preset mid-term maintenance time, and a medium fault occurs and the frequency exceeds 3 times / 100 hours;

[0012] Severe degradation state: The current of the three-phase AC motor reaches more than 95% of the rated current, the temperature exceeds 90°C, and the temperature reaches more than 90% of the temperature alarm threshold for more than 10 times, the cumulative running time exceeds the preset mid-term maintenance time, and a serious fault occurs and the frequency exceeds 2 times / 100 hours.

[0013] As a further solution of the present invention, in the central processing unit, the minor fault type, the medium fault type and the severe fault type referred to in the state identification method of the three-phase AC motor respectively include:

[0014] Minor fault type: current fluctuates but the fluctuation amplitude does not reach the set alarm threshold, vibration speed is greater than 3mm / s and less than or equal to 4mm / s, and noise is between 60 and 70 decibels;

[0015] Medium fault type: current fluctuates but the fluctuation amplitude exceeds 2% of the set alarm threshold, vibration speed is greater than 4mm / s and less than or equal to 7mm / s, and noise is between 70 and 80 decibels;

[0016] Serious fault types: short circuit, bearing failure, insulation material damage, insulation failure, coil burnout, and jamming.

[0017] As a further solution of the present invention, the processing flow of the dynamic programming model in the central processing unit includes:

[0018] Step 1, establish a state-decision model: use the human-computer interaction unit to set the parameters of the human-computer interaction, define the state space, and according to the set state classification, the decision set is divided into routine maintenance, preventive maintenance, enhanced maintenance and emergency maintenance for each state in each state space, respectively, for normal state, mild degradation state, moderate degradation state and severe degradation state;

[0019] Step 2, obtain the state transfer matrix: count the transition frequency between each state as the transition probability between states, use the Markov chain model to model the transition between states, matrix the transition probability of each state to form a state transfer matrix;

[0020] Step 3, calculating the cost function value: calculating the cost function value according to a preset cost function formula;

[0021] Step 4, dynamic programming recursive calculation: obtaining the expected cost in a set future period based on a preset dynamic programming recursive formula;

[0022] Step 5, obtain the global dynamic programming decision strategy: traverse the transition path of each state, and continuously update the decision of each state according to the minimum expected cost and transition probability identified for the current state until the global optimal dynamic programming decision strategy is obtained.

[0023] As a further solution of the present invention, the cost function formula involved in step 3 of the CPU unit dynamic programming model processing flow is:

[0024] C total (S i ,D j )=C m (S i ,D j )+C s (S i )·t+C f (S i )·P f (S i ,S i+1 )

[0025] Where: i and j are the state index and decision strategy index of the three-phase AC motor respectively, C total (S i ,D j ) is the three-phase AC motor in state S iNext, select Maintenance Decision D j The total cost, C m (S i ,D j ) is in state S i Next select D j The direct maintenance cost, C s (S i ) is in state S i The downtime loss cost when maintenance or failure occurs is t, which is the time required for maintenance or downtime, C f (S i ) is in state S i The failure cost of a failure without maintenance, P f (S i ,S i+1 ) is from the current state S i Transition to next state S i+1 The probability of failure occurring.

[0026] As a further solution of the present invention, the dynamic programming recursive formula involved in step 4 of the central processing unit dynamic programming model processing flow is:

[0027]

[0028] Where: V(S i ) is in state S i The minimum expected cost under the condition, k is the next state index, ranging from 1 to j, P ik From the current state S i Transition to next state S k The probability of V(S k ) is the next state S k The minimum expected cost, ∑ j Pi kV (S k ) is the selection decision D j After the state S i The weighted expected cost of transitioning to all other next states, To traverse all maintenance decisions D in the current state in the brackets j To find the minimum expected total cost.

[0029] As a further solution of the present invention, a fault maintenance method for a three-phase AC motor comprises the following steps:

[0030] Step 1: Data collection: Obtain the parameters of the three-phase AC motor, the power supply busbar status parameters, the energy storage system status parameters and the energy management demand data;

[0031] Step 2: Obtain the global optimal decision strategy: Identify the current state based on the parameters of the three-phase AC motor, form routine maintenance decisions, preventive maintenance decisions, enhanced maintenance decisions and emergency maintenance decisions based on historical data, count the transition frequencies between different states, use the Markov chain model to model the transition between states, obtain the state transition matrix, obtain the direct maintenance cost under the current state according to the cost function formula, use the dynamic programming recursive formula to traverse different maintenance decisions and all next states, and find the maintenance strategy with the lowest maintenance cost in the entire cycle. According to the minimum expected cost and state transition probability of each state, continuously update the maintenance decisions of each state, and finally determine the global optimal maintenance strategy. The current state of the three-phase AC motor and the recommended maintenance plan are displayed in real time through the human-computer interaction unit, and the cost of each maintenance decision and the specific data of each cost are displayed;

[0032] Step 3: Calculation and diagnosis processing of the central processing unit: diagnose whether the insulation value of the power supply busbar is greater than the safety insulation value. If so, allow the energy storage unit to perform charge and discharge operations, execute unit startup and operation, diagnose whether the power supply busbar voltage is within the allowable charge and discharge voltage range provided by the energy storage battery manufacturer. If within the range, allow the energy storage unit to perform charge and discharge operations, diagnose whether the energy storage battery charge and discharge voltage, charge and discharge current, resistance, and temperature are abnormal. If abnormal, disconnect the charge and discharge path of the energy storage battery, diagnose whether the charge and discharge state of the energy storage battery is abnormal. If the charge and discharge state does not match the expected state, it is judged to be abnormal and disconnect the storage battery. The charging and discharging paths of the energy storage battery are diagnosed to see whether the health status of the energy storage battery is abnormal. If abnormal, the charging and discharging paths of the energy storage battery are disconnected. The power of the energy storage battery is diagnosed to see whether it exceeds the power value set for charging. If it exceeds, the charging path of the energy storage battery is disconnected. The power of the energy storage battery is equal to the rated capacity of the battery pack multiplied by the current battery voltage, and then divided by the rated voltage. The start and stop instructions of the diagnosis system are diagnosed to see whether they are valid. If not, the charging and discharging paths of the energy storage battery are disconnected. The energy management signal is diagnosed to see whether it is valid. If not, the discharging path of the energy storage battery is disconnected. The real-time status and alarm display of the calculation and diagnosis results are performed on the human-computer interaction unit.

[0033] To solve the problems raised by the background technology, the technical effects of the platform and method proposed in the present invention are as follows: this platform and method for intelligent recovery and reuse of potential energy of marine engineering equipment effectively improves energy utilization and equipment operation efficiency, reduces dependence on fossil energy and carbon emissions, by removing traditional brake resistors, building energy storage systems and intelligent management mechanisms, while using dynamic programming and Markov chain models to monitor and optimize the status of three-phase AC motors in real time, dynamically adjusts maintenance cycles based on the global optimal maintenance strategy, reduces equipment failure rate and maintenance costs, ensures equipment safety and reliability, and improves the degree of personnel participation in equipment management based on the human-computer interaction unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The present invention is a schematic structural diagram of a potential energy recovery and fault maintenance platform for marine engineering equipment.

[0035] Figure 2 This is a flow chart of a potential energy recovery and fault maintenance platform for marine engineering equipment of the present invention; DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Example 1

[0038] like Figure 1As shown, a potential energy recovery and fault maintenance platform for marine engineering equipment proposed by the present invention includes a lifting device, a three-phase AC motor, a frequency converter and a brake resistor connected in sequence. The improvement is that the brake resistor is removed, the frequency converter is connected to a power supply busbar through a 1# fuse, the three-phase AC motor, the frequency converter, and the 1# fuse constitute an execution unit, the power supply busbar is connected to an isolating switch through a 2# fuse, the isolating switch is connected to a battery management system, the battery management system is connected to an energy storage battery, the 2# fuse, the isolating switch, the battery management system, and the energy storage battery constitute an energy storage unit, the battery management system and the execution unit are both connected to a central processing unit unit, the central processing unit unit is connected to a human-computer interaction unit, the battery management system is connected to an energy storage data acquisition module through a bus communication module, the central processing unit is connected to an energy management data acquisition module through a bus communication module, the power supply busbar is connected to a power supply busbar data acquisition module, the energy storage data acquisition module, the energy management data acquisition module, the power supply busbar The data acquisition module is composed of a data acquisition unit. The central processing unit collects the parameters of the three-phase AC motor to form a time series data set. According to the characteristics of the data, the three-phase AC motor is divided into several states. The transfer frequency between each state is counted as the transfer probability between states. The Markov chain model is used to model the transition between states. The transfer probability of each state is matrixed to form a state transfer matrix. The cost function is set to protect the sum of maintenance cost, downtime cost and failure cost. Through the state transfer matrix and the cost function, a dynamic programming model is constructed. According to the current state of the three-phase AC motor and the state transfer matrix, the dynamic programming recursive formula is used to obtain the expected cost of the next state, the transfer path of each state is traversed, and the maintenance cycle with the minimum total cost is dynamically selected. The human-computer interaction unit displays the current state of the three-phase AC motor, updates the maintenance suggestions in real time based on the dynamic programming results, and displays the cost function value of each maintenance decision and the data of each cost.

[0039] In this embodiment, the potential energy recovery process of the lifting equipment is as follows: when the heavy object is lowered or dragged by other systems, the lifting equipment is equipped with a three-phase AC motor. When the heavy object is lowered, the three-phase AC motor rotates in the opposite direction under the push of the heavy object and is in a generating braking state. The generated AC power is converted into DC through the inverter, and then transmitted to the power supply busbar through the 1# fuse of the execution unit, and then transmitted to the energy storage battery through the 2# fuse in the energy storage system for storage, so as to be used for the operation of other energy-consuming equipment.

[0040] In this embodiment, the process of potential energy recycling is as follows: the system potential energy stored in the energy storage battery is transmitted to the power supply busbar through the 2# fuse of the energy storage unit to store the DC power in the energy storage unit, and then transmitted to the inverter through the 1# fuse of the execution unit, thereby driving the three-phase AC motor of the terminal actuator, which can reduce the use of fossil energy and operating costs, improve energy utilization, and reduce carbon emissions.

[0041] The execution unit is both the final unit of system energy consumption and the unit for potential energy recovery; the three-phase AC motor is a device / installation driven by a frequency converter and is also a device / installation for converting working potential energy; one end of the frequency converter is connected to the three-phase AC motor and the other end is connected to the 1# fuse of the execution unit. It is a device / installation that rectifies the potential energy recovered by the three-phase AC motor into direct current; one end of the 1# fuse is connected to the frequency converter and the other end is connected to the power supply busbar. It is a device / installation that plays an overcurrent isolation protection role between the frequency converter and the power supply busbar.

[0042] One end of the power supply busbar is connected to the 1# fuse of the execution unit, and the other end is connected to the 2# fuse of the energy storage unit. It is a copper bar that receives the electric energy recovered by the execution unit and provides electric energy to the energy storage unit.

[0043] In the energy storage unit: one end of the 2# fuse is connected to the power supply busbar, and the other end is connected to the isolating switch, which is a device / device for overcurrent isolation protection of the energy storage unit; one end of the isolating switch is connected to the 2# fuse of the energy storage unit, and the other end is connected to the battery management system, which is a device / device for opening and closing control and isolation protection of the energy storage unit; one end of the battery management system is connected to the 2# fuse, and the other end is connected to the energy storage battery, which is a device / device for monitoring the energy storage battery; the energy storage battery includes several groups of energy storage battery packs, one end of which is connected to the battery management system, which is the final device / device for receiving and storing the electric energy recovered by the execution unit, and is also a device / device for providing system operation electric energy to the execution unit.

[0044] The functions of the human-computer interaction platform include: power supply bus status monitoring, battery charge and discharge control management, battery status monitoring, system alarm function setting, potential energy recovery control management, inverter operation status monitoring, three-phase AC motor operation status monitoring, system start and stop status monitoring, energy collaborative management, system alarm information management, system historical data management, and system user management.

[0045] Power supply busbar status monitoring is a real-time monitoring and display of the voltage and insulation value of the power supply busbar.

[0046] The battery charge and discharge control management includes a charge button and a discharge button with interlocking functions, which respectively send energy storage battery charging instructions and discharge instructions to the central processing unit.

[0047] Battery status monitoring is a real-time monitoring and display of the battery's voltage, current, resistance, temperature, available power, charge and discharge status, and fault status.

[0048] Potential energy recovery control management is controlled through the system start and stop buttons.

[0049] The inverter operation status monitoring is a real-time monitoring and display of the inverter's operation status, power, voltage, current, fault status, etc.

[0050] The three-phase AC motor operating status monitoring is a real-time monitoring and display of the motor's current, temperature, operating time, historical fault types and the historical frequency of each type of fault.

[0051] System start-stop status monitoring is a real-time monitoring display of the system start or stop status.

[0052] Energy collaborative management includes real-time monitoring and display of total power demand and total available power.

[0053] The system alarm function setting is to set the set value for the system alarm, including the set value for each device's overtemperature alarm, high temperature warning, abnormal voltage, and abnormal insulation value.

[0054] System alarm information management includes the management of over-temperature alarm, high temperature warning, inverter fault alarm, voltage abnormality alarm, insulation value abnormality alarm, and equipment status abnormality alarm for each device. It contains two modules: real-time alarm information and historical alarm information. Each alarm information contains relevant information such as event type, occurrence time, and event description.

[0055] System historical data management is the storage and historical data query function of the data collected by the data acquisition unit.

[0056] System user management is the management of login permissions for users of this platform. It is divided into three login user categories: administrator, operator, and browser. Users can switch login user categories according to actual needs. The current logged-in user type is monitored in real time and displayed on the human-computer interface.

[0057] Users can also use the visual human-computer interaction unit to manage energy storage batteries, inverters and other equipment according to actual conditions, making the management of the potential energy recovery and reuse platform more convenient, efficient and the operation and maintenance more transparent and accurate.

[0058] It should be noted that the three-phase AC motor parameters collected by the central processing unit include the current, temperature, operating time, historical fault types and historical frequency of each type of fault of the three-phase AC motor, and the status of the three-phase AC motor includes normal state, slightly degraded state, moderately degraded state and severely degraded state. In the central processing unit, the status identification method of the three-phase AC motor is specifically as follows:

[0059] Normal state: The current of the three-phase AC motor remains below 70% of the rated current, the temperature is between 0℃ and 70℃, the cumulative running time does not reach 80% of the preset mid-term maintenance time, and the frequency of minor faults is less than 5 times / 100 hours;

[0060] Mild degradation state: The current of the three-phase AC motor reaches 70% to 85% of the rated current, the temperature is between 70℃ and 80℃, the cumulative running time exceeds 80% of the preset mid-term maintenance time and has not reached the preset mid-term maintenance time, there are no major faults, and the frequency of minor faults is greater than or equal to 5 times and less than 20 times;

[0061] Moderate degradation state: The current of the three-phase AC motor reaches 85% to 95% of the rated current, the temperature is between 80°C and 90°C, and the number of times the temperature reaches 90% of the temperature alarm threshold is more than 3 times but less than 10 times, the cumulative running time exceeds 80% of the preset mid-term maintenance time and is less than or equal to the preset mid-term maintenance time, and a medium fault occurs and the frequency exceeds 3 times / 100 hours;

[0062] Severe degradation state: The current of the three-phase AC motor reaches more than 95% of the rated current, the temperature exceeds 90°C, and the temperature reaches more than 90% of the temperature alarm threshold for more than 10 times, the cumulative running time exceeds the preset mid-term maintenance time, and a serious fault occurs and the frequency exceeds 2 times / 100 hours.

[0063] It should be noted that in the central processing unit, the minor fault type, medium fault type and severe fault type referred to in the state identification method of the three-phase AC motor include:

[0064] Minor fault type: current fluctuates but the fluctuation amplitude does not reach the set alarm threshold, vibration speed is greater than 3mm / s and less than or equal to 4mm / s, and noise is between 60 and 70 decibels;

[0065] Medium fault type: current fluctuates but the fluctuation amplitude exceeds 2% of the set alarm threshold, vibration speed is greater than 4mm / s and less than or equal to 7mm / s, and noise is between 70 and 80 decibels;

[0066] Serious fault types: short circuit, bearing failure, insulation material damage, insulation failure, coil burnout, and jamming.

[0067] It should be noted that the processing flow of the dynamic programming model in the central processing unit includes:

[0068] Step 1, establish a state-decision model: use the human-computer interaction unit to set the parameters of the human-computer interaction, define the state space, and according to the set state classification, the decision set is divided into routine maintenance, preventive maintenance, enhanced maintenance and emergency maintenance for each state in each state space, respectively, for normal state, mild degradation state, moderate degradation state and severe degradation state;

[0069] Step 2, obtain the state transfer matrix: count the transition frequency between each state as the transition probability between states, use the Markov chain model to model the transition between states, matrix the transition probability of each state to form a state transfer matrix;

[0070] Step 3, calculating the cost function value: calculating the cost function value according to a preset cost function formula;

[0071] Step 4, dynamic programming recursive calculation: obtaining the expected cost in a set future period based on a preset dynamic programming recursive formula;

[0072] Step 5, obtain the global dynamic programming decision strategy: traverse the transition path of each state, and continuously update the decision of each state according to the minimum expected cost and transition probability identified for the current state until the global optimal dynamic programming decision strategy is obtained.

[0073] It should be noted that the cost function formula involved in step 3 of the CPU unit dynamic programming model processing flow is:

[0074] C total (S i ,D j )=C m (S i ,D j )+C s (S i )·t+C f (S i )·P f (S i ,S i+1 )

[0075] Where: i and j are the state index and decision strategy index of the three-phase AC motor respectively, C total (S i ,D j ) is the three-phase AC motor in state S i Next, select Maintenance Decision D j The total cost, C m (S i ,D j ) is in state S i Next select D j The direct maintenance cost, C s (S i ) is in state S i The downtime loss cost when maintenance or failure occurs is t, which is the time required for maintenance or downtime, C f (S i ) is in state S i The failure cost of a failure without maintenance, P f(S i ,S i+1 ) is from the current state S i Transition to next state S i+1 The probability of failure occurring.

[0076] This formula breaks down the total cost into direct maintenance cost, downtime loss cost, and failure cost, and combines the state transition probability for comprehensive evaluation, enabling the CPU to dynamically adjust the maintenance strategy under different states, thereby accurately selecting the most cost-effective maintenance solution. s (S i )·t, the central processing unit gives priority to the maintenance method with the least impact on operation, thereby minimizing unplanned downtime and ensuring the continuity of equipment operation. Failure cost P f (S i ) and state transition probability P f (S i ,S i+1 ) enables the system to identify high-failure risk states in advance and take preventive measures to reduce the possibility of serious failures and subsequent repair costs. By dynamically selecting low-risk maintenance cycles and maintenance strategies, the accumulated losses of motors in long-term operation are reduced and the life of the equipment is extended. Dynamic planning of comprehensive downtime costs, failure costs, and direct maintenance costs effectively balances the costs between operation and maintenance, significantly improving the economy of the system and improving the overall operational stability of the equipment.

[0077] It should be noted that the dynamic programming recursive formula involved in step 4 of the CPU unit dynamic programming model processing flow is:

[0078]

[0079] Where: V(S i ) is in state S i The minimum expected cost under the condition, k is the next state index, ranging from 1 to h, P ik From the current state S i Transition to next state S k The probability of V(S k ) is the next state S k The minimum expected cost, ∑ j P ik V(S k ) is the selection decision D j After the state S i The weighted expected cost of transitioning to all other next states, To traverse all maintenance decisions D in the current state in the brackets j To find the minimum expected total cost.

[0080] The dynamic programming recursive formula enables the CPU to i The total cost C total (S i ,D j ) and the transition probability P of the future state ik , dynamically evaluate all maintenance decisions D and determine the minimum expected cost path. By comprehensively considering the direct maintenance cost, downtime loss cost and failure risk cost of each state, and weightedly calculating the expected cost of each future state, this formula ensures that the most cost-effective maintenance strategy is selected when switching between states, significantly improving the accuracy and optimization of maintenance decisions, solving the problem that traditional maintenance methods are difficult to balance current and future costs, and avoiding resource waste caused by insufficient or excessive maintenance. Ultimately, it effectively reduces the total operating cost of the equipment throughout the entire operating cycle, improves the operating reliability and economy of the system, and reduces downtime and failure rate.

[0081] Example 2

[0082] like Figure 2 As shown, the present invention proposes a potential energy recovery and fault maintenance platform for marine engineering equipment, and a fault maintenance method for a three-phase AC motor includes the following steps:

[0083] Step 1: Data collection: Obtain the parameters of the three-phase AC motor, the power supply busbar status parameters, the energy storage system status parameters and the energy management demand data;

[0084] Step 2: Obtain the global optimal decision strategy: Identify the current state based on the parameters of the three-phase AC motor, form routine maintenance decisions, preventive maintenance decisions, enhanced maintenance decisions and emergency maintenance decisions based on historical data, count the transition frequencies between different states, use the Markov chain model to model the transition between states, obtain the state transition matrix, obtain the direct maintenance cost under the current state according to the cost function formula, use the dynamic programming recursive formula to traverse different maintenance decisions and all next states, and find the maintenance strategy with the lowest maintenance cost in the entire cycle. According to the minimum expected cost and state transition probability of each state, continuously update the maintenance decisions of each state, and finally determine the global optimal maintenance strategy. The current state of the three-phase AC motor and the recommended maintenance plan are displayed in real time through the human-computer interaction unit, and the cost of each maintenance decision and the specific data of each cost are displayed;

[0085] Step 3: Calculation and diagnosis processing of the central processing unit: diagnose whether the insulation value of the power supply busbar is greater than the safety insulation value. If so, allow the energy storage unit to perform charge and discharge operations, execute unit startup and operation, diagnose whether the power supply busbar voltage is within the allowable charge and discharge voltage range provided by the energy storage battery manufacturer. If within the range, allow the energy storage unit to perform charge and discharge operations, diagnose whether the energy storage battery charge and discharge voltage, charge and discharge current, resistance, and temperature are abnormal. If abnormal, disconnect the charge and discharge path of the energy storage battery, diagnose whether the charge and discharge state of the energy storage battery is abnormal. If the charge and discharge state does not match the expected state, it is judged to be abnormal and disconnect the storage battery. The charging and discharging paths of the energy storage battery are diagnosed to see whether the health status of the energy storage battery is abnormal. If abnormal, the charging and discharging paths of the energy storage battery are disconnected. The power of the energy storage battery is diagnosed to see whether it exceeds the power value set for charging. If it exceeds, the charging path of the energy storage battery is disconnected. The power of the energy storage battery is equal to the rated capacity of the battery pack multiplied by the current battery voltage, and then divided by the rated voltage. The start and stop instructions of the diagnosis system are diagnosed to see whether they are valid. If not, the charging and discharging paths of the energy storage battery are disconnected. The energy management signal is diagnosed to see whether it is valid. If not, the discharging path of the energy storage battery is disconnected. The real-time status and alarm display of the calculation and diagnosis results are performed on the human-computer interaction unit.

[0086] The fault maintenance method of the three-phase AC motor achieves efficient management of equipment operation and maximum utilization of resources by implementing a series of accurate diagnosis, decision-making and optimization processing. Specifically, the data acquisition step in the method ensures that the system obtains real-time parameter data of the three-phase AC motor, power supply busbar, energy storage system and energy management, providing an accurate data basis for subsequent optimization decisions. Based on these data, the central processing unit uses the Markov chain model and the state transition matrix to construct a state transition model of the three-phase AC motor, evaluates the transition probability of each state, and combines the cost function and dynamic programming formula to determine the global optimal maintenance strategy, which not only balances the maintenance cost, failure cost and downtime loss cost, finds the most cost-effective maintenance plan, but also dynamically adjusts the maintenance strategy to improve the reliability and economy of the system operation.

[0087] The Markov chain model mentioned in the present invention is a Markov chain in a finite state space.

[0088] In the calculation and diagnosis processing steps of the central processor, the system further ensures the safe operation of the energy storage unit by monitoring various indicators of the power supply bus, energy storage battery and energy management signal (by judging the insulation value and voltage of the power supply bus and other parameters, the system can decide whether to allow the charging and discharging operation of the energy storage unit, avoiding the potential damage to the battery by abnormal voltage; at the same time, by detecting the current, resistance and temperature of the energy storage battery and other parameters, the system can identify abnormal conditions and quickly disconnect abnormal paths, thereby reducing the risk of failure). This method displays the diagnosis results and maintenance suggestions in real time through the human-computer interaction unit, so that operators can understand the current equipment status and maintenance strategies in a timely manner, enhancing the intuitiveness and convenience of system management.

[0089] The total power of the lifting equipment is 2000KW, and the potential energy is recovered once a day, and the recovered power is 700W. The energy reduction ratio is 700 divided by 2000, which is equal to 30%. The average fuel consumption rate of the diesel engine is 210g / kW*h, the recovered power is 700KW, and the daily running time is 1 hour. The fuel reduction is 700 times 1 times 210 divided by 1000 square (unit conversion coefficient), which is equal to 0.147 tons. When the diesel price is 6000 yuan / ton, the fuel cost saved is 0.147 times 6000, which is equal to 882 yuan. When the carbon emission factor of the diesel engine is 3.15t CO2e / ton, the carbon emission reduction is 0.147 times 3.15, which is equal to 0.46t CO2e / ton. At the same time, predictive maintenance of the motor ensures the normal operation of the motor, reduces the system failure rate, prolongs its service life, and improves the system reliability. Assuming that the lifting equipment includes 10 three-phase AC motors, each motor costs RMB 100,000, and predictive maintenance extends the service life by 20%, RMB 200,000 can be saved. Based on the maintenance cost of RMB 100,000 for a single failure and the reduction of failures by 2 times per year, the failure maintenance cost can be reduced by at least RMB 2 million. It can be seen that the present invention can reduce fossil energy consumption, improve energy utilization, reduce carbon emissions, reduce equipment failure rate, extend the service life of equipment, and reduce ship operating costs.

[0090] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A potential energy recovery and fault maintenance platform for marine engineering equipment, comprising a lifting device, a three-phase AC motor, a frequency converter and a brake resistor connected in sequence, characterized in that: Remove the brake resistor, the inverter is connected to the power supply busbar through the 1# fuse, the three-phase AC motor, the inverter, and the 1# fuse constitute the execution unit, the power supply busbar is connected to the isolating switch through the 2# fuse, the isolating switch is connected to the battery management system, the battery management system is connected to the energy storage battery, the 2# fuse, the isolating switch, the battery management system, and the energy storage battery constitute the energy storage unit, the battery management system and the execution unit are both connected to the central processing unit, the central processing unit is connected to the human-computer interaction unit, the battery management system is connected to the energy storage data acquisition module through the bus communication module, the central processing unit is connected to the energy management data acquisition module through the bus communication module, the power supply busbar is connected to the power supply busbar data acquisition module, the energy storage data acquisition module, the energy management data acquisition module, and the power supply busbar data acquisition module constitute the data acquisition unit, and the central processing unit collects data from the three-phase AC motor. The parameters are used to form a time series data set. According to the characteristics of the data, the three-phase AC motor is divided into several states. The transfer frequency between each state is counted as the transfer probability between states. The Markov chain model is used to model the transition between states. The transfer probability of each state is matrixed to form a state transfer matrix. The cost function is set to make it the sum of the protection maintenance cost, downtime cost and failure cost. Through the state transfer matrix and the cost function, a dynamic programming model is constructed. According to the current state of the three-phase AC motor and the state transfer matrix, the dynamic programming recursive formula is used to obtain the expected cost of the next state, the transfer path of each state is traversed, and the maintenance cycle with the minimum total cost is dynamically selected. The human-computer interaction unit displays the current state of the three-phase AC motor, updates the maintenance suggestions in real time based on the dynamic programming results, and displays the cost function value of each maintenance decision and the data of each cost.

2. A potential energy recovery and fault maintenance platform for marine engineering equipment according to claim 1, characterized in that: The three-phase AC motor parameters collected by the central processing unit include the current, temperature, operating time, historical fault types and historical frequency of each type of fault of the three-phase AC motor. The status of the three-phase AC motor includes normal state, slightly degraded state, moderately degraded state and severely degraded state.

3. A potential energy recovery and fault maintenance platform for marine engineering equipment according to claim 2, characterized in that: In the central processing unit, the state identification method of the three-phase AC motor is specifically as follows: Normal state: The current of the three-phase AC motor remains below 70% of the rated current, the temperature is between 0℃ and 70℃, the cumulative running time does not reach 80% of the preset mid-term maintenance time, and the frequency of minor faults is less than 5 times / 100 hours; Mild degradation state: The current of the three-phase AC motor reaches 70% to 85% of the rated current, the temperature is between 70℃ and 80℃, the cumulative running time exceeds 80% of the preset mid-term maintenance time and has not reached the preset mid-term maintenance time, there are no major faults, and the frequency of minor faults is greater than or equal to 5 times and less than 20 times; Moderate degradation state: The current of the three-phase AC motor reaches 85% to 95% of the rated current, the temperature is between 80°C and 90°C, and the number of times the temperature reaches 90% of the temperature alarm threshold is more than 3 times but less than 10 times, the cumulative running time exceeds 80% of the preset mid-term maintenance time and is less than or equal to the preset mid-term maintenance time, and a medium fault occurs and the frequency exceeds 3 times / 100 hours; Severe degradation state: The current of the three-phase AC motor reaches more than 95% of the rated current, the temperature exceeds 90°C, and the temperature reaches more than 90% of the temperature alarm threshold for more than 10 times, the cumulative running time exceeds the preset mid-term maintenance time, and a serious fault occurs and the frequency exceeds 2 times / 100 hours.

4. A potential energy recovery and fault maintenance platform for marine engineering equipment according to claim 3, characterized in that: In the central processing unit, the state identification method of the three-phase AC motor refers to the minor fault type, the medium fault type and the serious fault type respectively include: Minor fault type: current fluctuates but the fluctuation amplitude does not reach the set alarm threshold, vibration speed is greater than 3mm / s and less than or equal to 4mm / s, and noise is between 60 and 70 decibels; Medium fault type: current fluctuates but the fluctuation amplitude exceeds 2% of the set alarm threshold, vibration speed is greater than 4mm / s and less than or equal to 7mm / s, and noise is between 70 and 80 decibels; Serious fault types: short circuit, bearing failure, insulation material damage, insulation failure, coil burnout, and jamming.

5. A potential energy recovery and fault maintenance platform for marine engineering equipment according to claim 4, characterized in that: The processing flow of the dynamic programming model in the CPU unit includes: Step 1, establish a state-decision model: use the human-computer interaction unit to set the parameters of the human-computer interaction, define the state space, and according to the set state classification, the decision set is divided into routine maintenance, preventive maintenance, enhanced maintenance and emergency maintenance for each state in each state space, respectively, for normal state, mild degradation state, moderate degradation state and severe degradation state; Step 2, obtain the state transfer matrix: count the transition frequency between each state as the transition probability between states, use the Markov chain model to model the transition between states, matrix the transition probability of each state to form a state transfer matrix; Step 3, calculating the cost function value: calculating the cost function value according to a preset cost function formula; Step 4, dynamic programming recursive calculation: obtaining the expected cost in a set future period based on a preset dynamic programming recursive formula; Step 5, obtain the global dynamic programming decision strategy: traverse the transition path of each state, and continuously update the decision of each state according to the minimum expected cost and transition probability identified for the current state until the global optimal dynamic programming decision strategy is obtained.

6. A potential energy recovery and fault maintenance platform for marine engineering equipment according to claim 5, characterized in that: The cost function formula involved in step 3 of the CPU unit dynamic programming model processing flow is: C total (S i ,D j )=C m (S i ,D j )+C s (S i )·t+C f (S i )·P f (S i ,S i+1 ) Where: i and j are the state index and decision strategy index of the three-phase AC motor respectively, C total (S i ,D j ) is the three-phase AC motor in state S i Next, select Maintenance Decision D j The total cost, C m (S i ,D j ) is in state S i Next select D j The direct maintenance cost, C s (S i ) is in state S i The downtime loss cost when maintenance or failure occurs is t, which is the time required for maintenance or downtime, C f (S i ) is in state S i The failure cost of a failure without maintenance, P f (S i ,S i+1 ) is from the current state S i Transition to next state S i+1 The probability of failure occurring.

7. A potential energy recovery and fault maintenance platform for marine engineering equipment according to claim 6, characterized in that: The dynamic programming recursive formula involved in step 4 of the CPU unit dynamic programming model processing flow is: Where: V(S i ) is in state S i The minimum expected cost under the condition, k is the next state index, ranging from 1 to j, P ik From the current state S i Transition to next state S k The probability of V(S k ) is the next state S k The minimum expected cost, ∑ j P ik V(S k ) is the selection decision D j After the state S i The weighted expected cost of transitioning to all other next states, To traverse all maintenance decisions D in the current state in the brackets j To find the minimum expected total cost.

8. The potential energy recovery and fault maintenance platform for marine engineering equipment according to claim 1, characterized in that: The fault maintenance method of three-phase AC motor includes the following steps: Step 1: Data collection: Obtain the parameters of the three-phase AC motor, the power supply busbar status parameters, the energy storage system status parameters and the energy management demand data; Step 2: Obtain the global optimal decision strategy: Identify the current state based on the parameters of the three-phase AC motor, form routine maintenance decisions, preventive maintenance decisions, enhanced maintenance decisions and emergency maintenance decisions based on historical data, count the transition frequencies between different states, use the Markov chain model to model the transition between states, obtain the state transition matrix, obtain the direct maintenance cost under the current state according to the cost function formula, use the dynamic programming recursive formula to traverse different maintenance decisions and all next states, and find the maintenance strategy with the lowest maintenance cost in the entire cycle. According to the minimum expected cost and state transition probability of each state, continuously update the maintenance decisions of each state, and finally determine the global optimal maintenance strategy. The current state of the three-phase AC motor and the recommended maintenance plan are displayed in real time through the human-computer interaction unit, and the cost of each maintenance decision and the specific data of each cost are displayed; Step 3: Calculation and diagnosis processing of the central processing unit: diagnose whether the insulation value of the power supply busbar is greater than the safety insulation value. If so, allow the energy storage unit to perform charge and discharge operations, execute unit startup and operation, diagnose whether the power supply busbar voltage is within the allowable charge and discharge voltage range provided by the energy storage battery manufacturer. If within the range, allow the energy storage unit to perform charge and discharge operations, diagnose whether the energy storage battery charge and discharge voltage, charge and discharge current, resistance, and temperature are abnormal. If abnormal, disconnect the charge and discharge path of the energy storage battery, diagnose whether the charge and discharge state of the energy storage battery is abnormal. If the charge and discharge state does not match the expected state, it is judged to be abnormal and disconnect the storage battery. The charging and discharging paths of the energy storage battery are diagnosed to see whether the health status of the energy storage battery is abnormal. If abnormal, the charging and discharging paths of the energy storage battery are disconnected. The power of the energy storage battery is diagnosed to see whether it exceeds the power value set for charging. If it exceeds, the charging path of the energy storage battery is disconnected. The power of the energy storage battery is equal to the rated capacity of the battery pack multiplied by the current battery voltage, and then divided by the rated voltage. The start and stop instructions of the diagnosis system are diagnosed to see whether they are valid. If not, the charging and discharging paths of the energy storage battery are disconnected. The energy management signal is diagnosed to see whether it is valid. If not, the discharging path of the energy storage battery is disconnected. The real-time status and alarm display of the calculation and diagnosis results are performed on the human-computer interaction unit.

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