Fault analysis method for ship health state management

Abstract

The invention discloses a fault analysis method for ship health state management, and belongs to the technical field of fault analysis in the field of ship fault prediction and health state management, and the method comprises the steps: analyzing fault mode influence and harmfulness FMECA; according to the fault influence of the FMECA, various symptom expressions caused by the fault reason are determined so as to determine the evolution characteristics of each fault mode; determining a sensor or a test point required for monitoring according to the fault symptom, and determining the position of the test point or the position of the sensor; performing fault diagnosis and prediction according to the selected test points or sensors and corresponding positions in combination with monitored signal parameters; and selecting preventive maintenance work or combination according to the fault reason and the fault symptom, and determining a maintenance interval period. Analysis can be actually carried out according to the health state management requirements of the equipment, analysis is carried out according to fault symptoms and fault characteristics, and meanwhile preventive maintenance measures are put forward to eliminate or reduce the potential fault occurrence opportunities.

Description

Technical Field

[0001] The present invention belongs to the technical field of fault analysis in the field of ship fault prediction and health status management, and in particular relates to a fault mode analysis method for ship health status management. Background Art

[0002] As a unit that independently performs missions at sea, ships often have longer mission durations than land and air equipment. During long-term high-intensity use, the technical status of various types of equipment will continue to deteriorate due to factors such as fatigue, aging, corrosion, and wear, resulting in reduced equipment performance or even failures, which will in turn have an adverse effect on the functions of various functional subsystems. In severe cases, it will lead to a decline in the ship's mission performance capability and a reduction in its technical status level.

[0003] With the continuous development of science and technology, the intelligence, informationization and integration of ship equipment are constantly improving, which brings difficulties to the fault diagnosis and rapid maintenance of ships. The maintenance strategy of existing ships is to adopt the form of preventive maintenance combined with post-maintenance, among which preventive maintenance is mainly regular maintenance. This maintenance strategy will lead to over-repair or under-repair of equipment, which is relatively low in cost-effectiveness and difficult to meet the needs of accurate ship protection. In order to solve the above problems, it is necessary to carry out research on fault prediction and health status technology. Health status management (HM) is an advanced equipment status management technology developed on the basis of equipment testability design. It is not only about testing, fault detection, isolation and maintenance of equipment, but also more comprehensive fault diagnosis and technical status management, which is a transition from equipment-level built-in test (BIT) to system-level comprehensive diagnosis and status management. To this end, it is necessary to carry out research on fault analysis methods for health status management, realize real-time grasp of the health status of equipment, transform maintenance strategies into status-based maintenance, and improve ship availability. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the present invention proposes a fault analysis method for ship health status management, which lays the foundation for fault analysis of ship health status management, selecting the most effective preventive maintenance measures, building a ship health status management system, and carrying out status-based maintenance.

[0005] To achieve the above object, the present invention provides a fault analysis method for ship health status management, comprising:

[0006] Analyze the failure mode effects and criticality FMECA to determine the product composition of the analyzed object, all corresponding failure modes, failure causes, and the severity level and probability of occurrence of the failure mode;

[0007] According to the failure impact of FMECA, determine the various symptoms caused by the failure cause to determine the evolution characteristics of each failure mode;

[0008] Determine the sensors or test points required for monitoring based on the fault symptoms, and determine the test point locations or sensor locations based on the products or functions involved in the fault symptoms and the signal processing characteristics;

[0009] According to the selected test points or sensors and their corresponding positions, combined with the monitored signal parameters, fault diagnosis and prediction are carried out;

[0010] Based on the cause of the fault and the symptoms of the fault, select the preventive maintenance work or combination and determine the maintenance interval period.

[0011] In some optional implementation schemes, the various symptoms caused by the failure cause are determined according to the failure impact of FMECA to determine the evolution characteristics of each failure mode, including:

[0012] Conduct basic information analysis on equipment failures, use hardware methods to analyze typical failure modes and codes, failure causes, and failure mechanisms of equipment or systems, analyze the impact of each failure mode, and determine the probability score and severity score of the failure mode.

[0013] In some optional implementation schemes, the selection of fault symptoms includes the following requirements:

[0014] The selected fault symptoms are universal and can be used as the key points to distinguish faults and can also be widely used for faults of the entire equipment;

[0015] The selected fault symptom measurement values ​​are easy to obtain and are also widely recognized by experts and users;

[0016] The selected fault symptoms should be independent and should be distinguished from other symptoms and belong to different types.

[0017] The selected fault symptoms must take into account individual characteristics while ensuring that the entire diagnostic sample size is appropriate.

[0018] In some optional implementation schemes, determining the sensors or test points required for monitoring according to the fault symptom, and determining the test point locations or sensor locations according to the products or functions involved in the fault symptom and signal processing characteristics, includes:

[0019] Based on the fault symptoms, determine the sensitive test parameters and sensor types that can characterize the health status of the equipment, and at the same time determine the location of the test point or the sensor layout. For large equipment or systems, it is necessary to determine the subsystem or equipment where the sensor is located, as well as the components or parts to which the sensor is attached.

[0020] In some optional implementation schemes, when selecting sensors, the selection is made based on measurement performance, availability, energy consumption, cost and environmental conditions, and the selection follows the principles of object matching, high reliability, low power consumption, easy installation and high integration.

[0021] In some optional implementation schemes, the fault diagnosis and prediction method includes qualitative analysis method and quantitative analysis method, wherein the qualitative analysis method includes: graph theory method, expert system and qualitative simulation; the quantitative analysis method includes: analytical model-based method and data-driven method.

[0022] In some optional implementation schemes, the preventive maintenance work includes: maintenance, operator monitoring, usage inspection, function testing, scheduled overhaul, scheduled scrapping and comprehensive work; the combination of the preventive maintenance work is sorted by resource consumption, cost, maintenance difficulty and maintenance depth, and is selected from front to back.

[0023] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0024] The present invention can analyze the actual health status management needs of the equipment, analyze the fault signs and characteristics, and propose fault diagnosis and fault prediction methods to lay the foundation for establishing a health status management system to carry out condition-based maintenance. At the same time, preventive maintenance measures are proposed to eliminate or reduce the chances of potential failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a fault analysis flow chart for health status management provided by an embodiment of the present invention;

[0026] Figure 2 This is a sensor selection process provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0028] The present invention provides a ship health status management-oriented fault analysis method, which includes determining basic fault information, analyzing fault symptoms and fault evolution characteristics, determining test points / sensors and layout, analyzing fault diagnosis and fault prediction methods, and proposing preventive maintenance measures. The specific steps are as follows:

[0029] 1) Determine the basic fault information

[0030] Conduct basic Failure Mode, Effects and Criticality Analysis (FMECA) to determine basic information such as the product composition of the analyzed object, all corresponding failure modes, causes of failures, severity level of failure modes, and probability level of occurrence.

[0031] 2) Analyze fault symptoms and fault evolution characteristics

[0032] According to the failure impact of FMECA, combined with empirical data, principle analysis and (or) available simulation analysis methods, the various symptoms caused by the cause of the failure are determined, and the evolution characteristics of each failure mode are determined based on experience or relevant data and the occurrence mechanism of the failure.

[0033] 3) Determine test points / sensors and layout

[0034] Determine the sensors or test points required for monitoring based on the fault precursors, and determine the test point locations or sensor locations based on the products / functions involved in the fault precursors and signal processing characteristics to obtain the best test results.

[0035] 4) Analyze fault diagnosis and fault prediction methods

[0036] Based on the selected test points / sensors and their layout, and the monitored signal parameters, select feasible fault diagnosis technical methods for fault mode diagnosis. Based on the fault signals collected by the sensors used, select feasible signal processing and other fault diagnosis and prediction methods.

[0037] 5) Determine preventive maintenance measures

[0038] According to the cause and symptoms of the failure, select the most appropriate preventive maintenance work or its combination, and determine its maintenance interval to reduce the probability and impact of the failure.

[0039] Specifically, Figure 1 As shown, the following combination Figure 1 , describing the process of failure mode analysis for ship health management.

[0040] In step 1, carry out basic information analysis of equipment failures as required, use hardware methods to analyze typical failure modes and codes (when necessary), failure causes, and failure mechanisms of equipment / systems, and analyze the failure impacts (including local impacts, higher-level impacts, and final impacts) of each failure mode, and determine the probability score and severity score of the failure mode, as shown in Tables 1 and 2.

[0041] Table 1 Severity rating table

[0042]

[0043] Table 2 Failure mode occurrence probability score table

[0044] Occurrence probability score Likelihood of Occurrence <![CDATA[Probability of occurrence of failure mode P m Reference range]]> 1 Very low <![CDATA[P m ≤10 -6 ]]> 2，3 Lower <![CDATA[1×10 -6 <P m ≤1×10- 4 ]]> 4，5，6 medium <![CDATA[1×10-4<P m ≤1×10 -2 ]]> 7，8 high <![CDATA[1×10 -2 <P m ≤1×10 -1 ]]> 9，10 Very high <![CDATA[P m >1×10-1]]>

[0045] In step 2, based on the cause of the failure, combined with empirical data, principle analysis and (or) available simulation analysis methods, analyze and determine the performance of the fault precursor of each failure mode, i.e., the fault symptom. The main requirements for the selection of fault symptom are:

[0046] (1) The selected fault symptoms should be universal enough to be used as the key points to distinguish faults and be widely applicable to the faults of the entire equipment;

[0047] (2) The selected fault symptom measurement values ​​are easy to obtain without overly complicated means and are also widely recognized by experts and users;

[0048] (3) The selected fault symptoms should have a certain degree of independence. In addition to being well distinguished from other symptom points, they should also belong to different types;

[0049] (4) The selected fault symptoms must take into account individuality while ensuring that the entire diagnostic sample size is appropriate. If a symptom only appears in a certain type of fault, although it can be well distinguished from other faults, too many symptom points of this type may make the fault description overly redundant.

[0050] At the same time, based on experience or relevant data and the laws of fault occurrence, the evolution characteristics of each fault mode are determined, and the fault modes are classified into sudden faults and gradual faults.

[0051] In step 3, based on the fault signs analyzed in the previous step, determine the sensitive test parameters and sensor types that can characterize the health status of the equipment. At the same time, determine the location of the test point or the sensor layout. For large equipment or systems, determine the subsystem or equipment where the sensor is located, as well as the components / parts to which the sensor is attached.

[0052] There are many types of sensors that can be used for monitoring equipment. The principles and structures of sensors vary greatly. How to comprehensively select sensors based on specific monitoring objects, measurement purposes, and measurement environments is the first problem to be solved in sensor configuration and application. When selecting sensors, the main factors to consider are measurement performance, availability, energy consumption, cost, and environmental conditions. The selection should follow the principles of object matching, high reliability, low power consumption, ease of installation, and high integration. The general process of sensor selection is as follows: Figure 2 shown.

[0053] In step 4, according to the selected test points / sensors and layout, select feasible fault diagnosis technology methods, such as BIT, external automatic test, manual test, etc., and select the most effective fault diagnosis and prediction method based on the monitored equipment status signals and characteristic parameters, combined with the understanding of the equipment and the accumulation of historical operation and fault data. Fault diagnosis and prediction methods are divided into qualitative analysis methods and quantitative analysis methods, as follows:

[0054] Qualitative analysis method:

[0055] (1) Graph theory method: symbolic directed graph, fault tree, etc., which are simple to model and easy to understand;

[0056] (2) Expert systems: traditional expert systems, fuzzy expert systems, and confidence rule base expert systems, which utilize the rich experience and knowledge of experts without the need for mathematical modeling of the system;

[0057] (3) Qualitative simulation: Qualitative simulation based on qualitative differential equation constraints can infer the dynamic behavior of the equipment;

[0058] Quantitative analysis method:

[0059] (1) Analytical model-based methods: state estimation, parameter estimation, equivalent space, and deep knowledge of the device

[0060] (2) Data-driven methods: machine learning, information fusion, and signal processing. They do not require precise analytical models of the process and start from the historical data of the equipment.

[0061] In step 5, based on the specific cause and symptom of the fault, select the most effective preventive maintenance measures and determine the time interval or time point for its implementation. Preventive maintenance measures are divided into seven categories: maintenance, operator monitoring, use inspection, function inspection, scheduled overhaul, scheduled scrapping, and comprehensive work.

[0062] (1) Maintenance: Operations such as surface cleaning, wiping, ventilation, adding oil or lubricant, inflation, tightening and adjustment performed to maintain the inherent design performance of the equipment, but excluding functional testing and usage inspection.

[0063] (2) Operator monitoring: The monitoring of the equipment status by the operator during normal use, with the purpose of discovering potential equipment failures, including:

[0064] 1) Inspection of the equipment before use;

[0065] 2) Monitoring of equipment instruments;

[0066] 3) Identify abnormal phenomena or potential faults through senses, such as by identifying changes in smell, sound, vibration, temperature, appearance, operating force, etc. to promptly discover abnormal phenomena or potential faults.

[0067] (3) Operational inspection: A planned qualitative inspection (or observation) to determine whether the equipment can perform its specified functions. Its purpose is to discover hidden functional failures, ensure the availability of specified hidden functions, and minimize the possibility of multiple failures.

[0068] (4) Functional testing: A planned quantitative inspection to determine whether the functional parameters of the equipment are within the specified limits. Its purpose is to detect potential faults.

[0069] (5) Regular overhaul: Equipment should be overhauled after a specified period of use to restore it to the specified state.

[0070] (6) Scheduled scrapping: Equipment will be scrapped after a specified period of use.

[0071] (7) Integrated work: implementation of two or more types of preventive maintenance work.

[0072] The above preventive maintenance work types are arranged according to their resource consumption, cost, maintenance difficulty, and maintenance depth, so they should be selected from the first to the last.

[0073] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0074] It will be easily understood by those skilled in the art that 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 and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A fault analysis method for ship health status management, characterized in that: include: Analyze the failure mode effects and criticality FMECA to determine the product composition of the analyzed object, all corresponding failure modes, failure causes, and the severity level and probability of occurrence of the failure mode; According to the failure impact of FMECA, determine the various symptoms caused by the failure cause to determine the evolution characteristics of each failure mode; Determine the sensors or test points required for monitoring based on the fault symptoms, and determine the test point locations or sensor locations based on the products or functions involved in the fault symptoms and the signal processing characteristics; According to the selected test points or sensors and their corresponding positions, combined with the monitored signal parameters, fault diagnosis and prediction are carried out; Based on the cause of the fault and the symptoms of the fault, select the preventive maintenance work or combination and determine the maintenance interval period.

2. The method according to claim 1, characterized in that According to the failure impact of FMECA, the various symptoms caused by the failure cause are determined to determine the evolution characteristics of each failure mode, including: Conduct basic information analysis on equipment failures, use hardware methods to analyze typical failure modes and codes, failure causes, and failure mechanisms of equipment or systems, analyze the impact of each failure mode, and determine the probability score and severity score of the failure mode.

3. The method according to claim 2, characterized in that The selection of fault symptoms includes the following requirements: The selected fault symptoms are universal and can be used as the key points to distinguish faults and can also be widely used for faults of the entire equipment; The selected fault symptom measurement values ​​are easy to obtain and are also widely recognized by experts and users; The selected fault symptoms should be independent and should be distinguished from other symptoms and belong to different types. The selected fault symptoms must take into account individual characteristics while ensuring that the entire diagnostic sample size is appropriate.

4. The method according to claim 3, characterized in that Determining the sensors or test points required for monitoring according to the fault symptom, and determining the test point location or sensor location according to the product or function involved in the fault symptom and the signal processing characteristics, includes: Based on the fault symptoms, determine the sensitive test parameters and sensor types that can characterize the health status of the equipment, and at the same time determine the location of the test point or the sensor layout. For large equipment or systems, it is necessary to determine the subsystem or equipment where the sensor is located, as well as the components or parts to which the sensor is attached.

5. The method according to claim 4, characterized in that When selecting sensors, we make our selection based on measurement performance, availability, energy consumption, cost, and environmental conditions, and follow the principles of object matching, high reliability, low power consumption, ease of installation, and high integration.

6. The method according to any one of claims 1 to 5, characterized in that: The fault diagnosis and prediction method includes a qualitative analysis method and a quantitative analysis method, wherein the qualitative analysis method includes: a graph theory method, an expert system and a qualitative simulation; the quantitative analysis method includes: a method based on an analytical model and a data-driven method.

7. The method according to claim 6, characterized in that The preventive maintenance work includes: maintenance, operator monitoring, usage inspection, function testing, scheduled disassembly and repair, scheduled scrapping and comprehensive work; the combination of the preventive maintenance work is sorted according to consumption of resources, cost, maintenance difficulty and maintenance depth, and is selected from front to back.

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