Inspection Intervals Research Articles

Optimal condition-based maintenance policy considering nested conditional value-at-risk and operational availability: A case study on semiconductor manufacturing equipment

To address concerns regarding economic risks and reliability issues in existing maintenance practices, this study introduces a novel condition-based maintenance model that considers failure risks in terms of both cost and availability. Utilizing a Markov decision process, this model determines inspection intervals and maintenance policies aimed at minimizing the nested conditional Value-at-Risk of cumulative costs while satisfying operational availability constraints. By applying this model to the plasma etching process, we demonstrate its effectiveness compared with existing maintenance models. Additionally, we found that higher risk levels do not necessarily lead to stricter maintenance policies, whereas achieving better operational availability incurs additional costs. These findings highlight the importance of balancing cost and availability risks when determining an optimal maintenance policy.

Opportunistic maintenance for a novel load-sharing system with dependent degradation rate and volatility

For traditional degrading load-sharing systems, a typical assumption lies in the only effect of unit load on its degradation rate. However, motivated by a laser system example with positive correlation between degradation rate and volatility, we present a novel load-sharing system with general degradation rate-volatility-load correlation. Furthermore, to take account for unit economic dependence caused by sharable maintenance set-up cost, and the window period generated by the maintenance on some unit, opportunistic maintenance (OM) strategy is introduced to offer the opportunity of simultaneously maintaining another working unit under control-limit criteria. Based on the semi-regenerative process, the long-run average cost rate is minimized to obtain the optimal inspection interval, preventive maintenance and OM thresholds. Compared to the traditional condition-based maintenance strategy separately for each unit, the effectiveness of OM strategy is validated through numerical examples and case studies, offering broad managerial insights for engineers to reduce maintenance expenses.

Probabilistic risk assessment of civil aircraft associated failures under condition-based maintenance

Maintenance can improve an aircraft system's reliability over a long operation period or when a component has failed. However, inappropriate maintenance inspection intervals will cause latent failures to be covered or undetected, leading to a large number of unplanned flight disruptions for airlines. In this paper, we present a two-stage framework to assess the associated failure risk of civil aircraft under condition-based maintenance. In the first stage of the framework, the probability of primary functional failure across the lifecycles of the monitored component is determined by analyzing whether the current inspection interval prevents the component from progressing from latent failure to functional failure. In the second stage of the framework, the associated failure probability between components and related systems is formulated by the adjacency matrix. The structure and performance of the proposed model were tested on a case study by run-to-failure data associated with aircraft engines from a large airline. Focusing on the scenario of turbine disk cracking leading to fragment penetration of the fuel tank and causing fire as the consequential fault impact path, the results show that the risk of aircraft fire caused by turbine disk fragments falls within an acceptable range, necessitating the completion of inspections and subsequent monitoring within the stipulated timeframe. The method can be used to readjust the inspection interval, optimize the operation plan, improve the on-time performance of flights, and reduce the risk of aviation accidents.

Condition-based maintenance policy for shared service-oriented leased equipment

Due to growing consumer demands for immediacy, personalization, and high availability, equipment manufacturers are venturing into the sharing market with expanded shared leasing services. While selling usage rights of equipment, they provide additional services to the consumer in the role of equipment operators, such as free maintenance and swap. However, uncertain usage rates and return times in the shared model pose significant maintenance challenges. This paper proposes a personalized condition-based maintenance (CBM) policy accounting for these uncertainties and heterogeneous consumer usage patterns. By considering inspection, maintenance, and penalty costs, we minimize the long-run expected maintenance cost per unit of time for operators. In the numerical example, we compare our policy against fixed inspection interval CBM policy and non-fixed inspection interval CBM under average usage rate, demonstrating that personalized CBM reduces maintenance costs. Sensitivity analysis provides some managerial insights. Firstly, operators should adjust the number of free swaps strategically to manage the intensity of equipment swaps, and strike a balance between the increased maintenance costs due to the swap intensity enhancement and the improved user satisfaction and equipment availability resulting from the increased number of free swaps. Secondly, when the acceleration effect of imperfect maintenance on the degradation rate is strengthened, operators should prioritize reducing the frequency of imperfect maintenance and increasing the frequency of perfect preventive maintenance. Moreover, operators must make strategic investments in their maintenance actions, balancing maintenance costs and penalty costs to develop cost-effective imperfect maintenance improvement factors.

Cost-based performance optimization of a single system under a hierarchical imperfect maintenance policy

Abstract Due to the influence from external factors such as repair sites and seasonal climate change, it is difficult to restore the system performance to any intermediate level between perfect maintenance and minimal maintenance through imperfect maintenance. This article first categorizes the states of the system just before repair into three classes based on its internal degradation level: failure, major defect, and minor defect, with three corresponding thresholds. Subsequently, the corresponding repairs are carried out to dwindle the system’s degradation to different levels. In detail, if the internal degradation level of the system just before repair is recognized as minor defect, an imperfect repair, termed as type I imperfect repair, is implemented to scale down the degradation level below the minor defect threshold. If the degradation level is identified as major defect, an upgraded imperfect repair, termed as type II imperfect repair, is executed to only lower the degradation to the level between the minor defect threshold and major defect threshold. Otherwise, if the degradation before repair is beyond the failure threshold, replacement will be carried out instead of these imperfect repairs. Thus, a novel hierarchical imperfect maintenance structure is introduced. Then, a multi-variable repair cost model is constructed when considering the related costs incurred from inspection, type I imperfect repair, type II imperfect repair, replacement, and even system downtime. Finally, with the aid of the stationary law of Markov chains and the semi-regenerative process, the cost-based performance optimization with three parameters, including the inspection interval, minor defect threshold, and major defect threshold, is explored through a numerical experiment, and the closed expression of the optimal cost rate is provided.

Dynamic predictive maintenance strategy for multi‐component system based on LSTM and hierarchical clustering

AbstractIn recent years, there has been growing interest in employing predictive methods to forecast the remaining useful life of industrial equipment. However, the challenge lies in how to take advantage of the dynamic predictive information to facilitate the maintenance of decision‐making. This problem becomes particularly challenging for complex industrial systems consisting of multiple components with economic dependencies. This paper aims at providing an effective maintenance strategy for multi‐component systems based on predictive information, while considering economic dependencies among different system components. To this end, a dynamic predictive maintenance (PdM) strategy that minimizes the mean maintenance cost over a decision period is proposed, where both long‐term and short‐term policies are integrated into the decision‐making framework. Specifically, the long‐term policy is formulated using predictions derived from historical degradation data through a Long Short‐Term Memory (LSTM) model. Concurrently, real‐time monitoring data is employed to forecast imminent degradation in components, serving as a basis for determining the necessity of short‐term adjustments. This paper embeds the consideration of economic dependencies among components within the maintenance strategy design and employs hierarchical clustering to establish an effective and efficient maintenance grouping policy. The experimental results demonstrate that our proposed strategy significantly outperforms conventional approaches, including block‐based and age‐based maintenance, resulting in substantial cost savings. The proposed strategy is also compared with a similar version without grouping, and the results verify the added value of the optimal maintenance grouping policy in cost reduction. Moreover, a comprehensive analysis of the proposed method is provided, including the impact of different inspection costs and inspection intervals on maintenance decision‐making, which can provide insightful guidance to various PdM scenarios in practice.

The Optimal Experimental Design for Exponentiated Frech’et Lifetime Products

In many manufacturing industries, the lifetime performance index CL is utilized to assess the manufacturing process performance for products following some lifetime distributions and subjecting them to progressive type I interval censoring. This paper aims to explore the sampling design required to achieve a specified level of significance and test power for products with lifetimes following the Exponentiated Frech’et distribution. Since lifetime distribution is an asymmetrical probability distribution, this investigation is related to the topic of asymmetrical probability distributions and applications in various fields. When the termination time is fixed but the number of intervals is variable, the optimal number of inspection intervals and sample sizes yielding the minimized total experimental costs are determined and tabulated. When the termination time is varying, the optimal number of inspection intervals, sample sizes, and equal interval lengths achieving the minimum total experimental costs are determined and tabulated. Optimal parameter values are displayed in tabular form for feasible applications for users. Additionally, a practical example is provided to illustrate how this sampling design can be used to collect data by using the optimal setup of parameters, followed by a testing procedure to assess the capability of the production process.

Hydrogen embrittlement effects on remaining life and fatigue crack growth rate in API 5L X52 steel pipelines under cyclic pressure loading

Transporting hydrogen gas through the existing natural gas pipeline network offers an efficient solution for energy storage and conveyance. Hydrogen generated from excess renewable electricity can be conveyed through the API 5L steel-made pipelines that already exist. In recent years, there has been a growing demand for the transportation of hydrogen through existing gas pipelines. Therefore, numerical and experimental tests are required to verify and ensure the mechanical integrity of the API 5L steel pipelines that will be used for pressurized hydrogen transportation. Internal pressure loading is likely to accelerate hydrogen diffusion through the internal pipe wall and consequently accentuate the hydrogen embrittlement of steel pipelines. Furthermore, pre-cracked pipelines are susceptible to quick failure mainly under a time-dependent cyclic pressure loading that drives fatigue crack propagation. Meanwhile, after several loading cycles, the initial cracks will propagate to a critical size. At this point, the remaining service life of the pipeline can be estimated, and inspection intervals can be determined. This paper focuses on the hydrogen embrittlement of API 5L steel-made pipeline under cyclic pressure loading. Pressurized hydrogen gas is transported through a network of pipelines where demands at consumption nodes vary periodically. The resulting pressure profile over time is considered a cyclic loading on the internal wall of a pre-cracked pipeline made of API 5L steel-grade material. Numerical modeling has allowed the prediction of fatigue crack evolution and estimation of the remaining service life of the pipeline. The developed methodology presented in this paper is based on the ASME B31.12 standard, which outlines the guidelines for hydrogen pipelines with reference to the ASME BPVC Section VIII, Division 3, Article KD-10.

Bayesian-informed fatigue life prediction in shallow shell structures with the dual boundary element method

This study introduces an innovative approach for statistically inferring the Equivalent Initial Flaw Size Distribution (EIFSD) in shallow shell structures with the aim of predicting fatigue life. The proposed approach integrates Bayesian inference and surrogate modelling techniques, and utilizes a long crack growth model. The EIFSD is crucial for estimating fatigue life and ensuring structural durability. To showcase the proposed methodology, a numerical example featuring a fuselage window structure under multi-axial loading, employing the Dual Boundary Element Method (DBEM) for crack growth modelling, is presented. Efficiency gains in the inference of the EIFSD are achieved through the development of surrogate models, notably a Co-Kriging model that significantly reduces computational time and outperforms a Kriging model in error reduction. The study explores two assumptions for inspection data sourcing, leading to enhanced methodological effectiveness across varying uncertainty levels. It considers the separate effects of Paris Law constants and loading conditions under increased uncertainty, presenting cases that demonstrate the Co-Kriging model’s high accuracy and computational efficiency. The proposed methodology’s robustness is further validated by assessing the impact of prior distribution quality on Bayesian inference, showing the method’s adaptability to high uncertainty while maintaining a good level of accuracy. This study also demonstrates an approach for fatigue life estimation of structures using the inferred EIFSD. By evaluating the structure up to a critical crack length, the methodology allows for the accurate estimation of fatigue life, which, in turn, facilitates the determination of optimal inspection intervals. Notably, under conditions of high uncertainty, the method showcased a maximum difference of 1.51% in the estimated fatigue life when compared to the true EIFSD, underscoring the technique’s high accuracy and reliability.

Joint optimization of preventive maintenance and triggering mechanism for k-out-of-n: F systems with protective devices based on periodic inspection

Systems with protective devices (PDs) have attracted increasing research interests due to their extensive engineering applications. In practice, the PDs are triggered to function under certain conditions. However, existing studies focus on the reliability modelling and analysis for such systems, and the joint optimization of maintenance and PD's triggering mechanism has not been investigated. Consequently, this paper explores the joint optimization of maintenance and triggering mechanism for k-out-of-n: F systems with PDs. A new triggering mechanism of the PD is proposed based on the number of failed or unhealthy components. The working PD can reduce the impacts of shocks on components. Based on the detected system status through periodic inspections, corresponding actions including corrective maintenance for components or the PD, preventive maintenance for components or shutting down the PD can be decided to improve the system reliability. The operation process of the established system is modelled by Markov decision process, and optimal actions under diverse system states are obtained via value iteration algorithm. The optimal inspection interval and triggering mechanism are derived by solving the optimization model aiming to minimize the expected total cost. Finally, the applicability of the proposed model is validated by abundant numerical examples.

Novel condition-based opportunistic maintenance policy for series systems with dependent competing failure processes

This paper investigates a novel condition-based opportunistic maintenance model for a two-component series system. The system comprises two non-identical components, that is, Component 1 and Component 2, suffering from shocks with different intensities. The two components are subject to dependent competing failure processes, that is, soft and hard failures. The component fails whichever type of failure process occurs. At each inspection, the replacement decision for components is made according to the condition of both components. In addition, when Component 1 is preventively replaced and Component 2 reaches the opportunistic maintenance threshold, opportunistic maintenance (OM) is implemented on Component 2. The optimal inspection interval, preventive replacement (PR) threshold, and OM threshold are derived by minimizing the long-run expected maintenance cost rate. Finally, the superiority of the proposed maintenance model is verified by an illustrative example.

A Reliability Assessment Method for Natural Gas Pipelines with Corroded Defects That Considers Detection Cycles

With the development of natural gas pipelines, the proportion of aged pipelines in service has been increasing, and corrosion remains a primary cause of pipeline failure. Regular inspections and reliability assessments are crucial to ensure the safe operation of pipelines. This study investigated an efficient reliability assessment method for corroded pipelines that considers in-line inspection intervals. First, this study compared the commonly used limit state equations for corrosion defects to select one suitable for X80-grade steel pipelines. Additionally, a Tail-Fit Monte Carlo Simulation (TF-MCS) algorithm was proposed to improve the computational speed by 30 times compared to traditional Monte Carlo simulations. Then, this study explored the inspection intervals used for reliability assessments of corroded pipelines. Finally, the parameter sensitivity was analyzed considering the yield strength, maximum operating pressure, and pipe diameter. This study ensures the reliable operation of corroded gas pipelines.

SensorDisc - a novel approach to in-situ assessment of bolt load and condition.

While Structural Health Monitoring has become widespread and well-accepted for ensuring safety and longevity of buildings and facilities, the condition of fasteners in said structures is oftentimes not considered for holistic condition assessment. This is not representative of the role fixings (in particular, bolt connections) play, as they experience high loads, fatigue, and corrosion, with the potential for causing costly consequences in case of failure. In practice, the condition assessment of bolts is usually carried out by periodic re-tightening and manual documentation of the torque and apparent condition. This procedure is not only labor-intensive but also fails to provide deeper insights into the condition of the bolted connection itself. Furthermore, in many cases, the inspection intervals are not tailored to the specific needs of projects and structures but are based on general assumptions, which necessarily represent a compromise between costly over-caution and accepting blind spots. One possible reason for not making more widespread use of monitoring technology in this case is the high cost and complexity of conventional bolt monitoring, where individual bolts are manually fitted with load sensors, and data processing is performed by highly skilled professionals. A solution for this dilemma is presented in the form of the fischer SensorDisc, which offers a fully integrated, robust, and convenient option for bolt load assessment. One of the key features that distinguish the SensorDisc is its dedicated design for use in industrial and construction environments. Not only can it be used in a variety of bolt connections, either as original equipment or as a retrofit, but it also does not require special tools for readout, as this can be accomplished with a common smartphone. For maximized ease of use, data and device management are all offered through a cloud-based service that can be accessed via an app, browser, or API.

Health Management of Aircraft Fuel Systems: A Practical Prognostic Perspective

Aircraft health management, specifically pertaining to fuel systems, is a comprehensive process that is undertaken to ensure the continuous airworthiness of the aircraft over its entire lifespan. The integration of health management into aircraft systems requires a methodical strategy to quantitatively evaluate the possible advantages provided by each specific application. Gaining a thorough comprehension of the advantages linked to integrated health management systems provides useful insights for enhancing the design of these systems and formulating efficient prognostic techniques. The role of prognosis in the field of prognostics and health management (PHM) is essential for improving the dependability of systems. Prognosis involves accurately predicting the remaining useful life (RUL) within the domain of integrated health management. This study improves the precision of prediction results, optimises the derived prerequisites for health management in the specialised field of RUL prediction for aircraft fuel systems, provides a thorough understanding, and advances the grasp of the previously described requirements in a more effective way. Additionally, it presents the most suitable solutions in order to address these demands by utilising prognostic techniques. Furthermore, this study presents an analysis of the proposed hybrid prognostic approach in the setting of controlled laboratory conditions. The applications encompassed in this work consist of three main areas. Firstly, the evaluation of inspection interval modifications is conducted by taking into account load and environmental monitoring data. Secondly, design considerations for damage prognostics are incorporated into the analysis. Lastly, a design modification is presented as an example of how condition-based maintenance (CBM) can be facilitated. A methodology that enables the efficient and well-founded construction of a health management system, specifically designed for aircraft fuel systems, is presented. This methodology, together with its associated outcomes, presents a compelling advantage in terms of lowering life cycle expenses while simultaneously enhancing availability, dependability, and performance.

Application of deep learning for structural health monitoring of a composite overwrapped pressure vessel undergoing cyclic loading

Structural Health Monitoring (SHM) using ultrasonic-guided waves (UGWs) enables continuous monitoring of components with complex geometries and provides extensive information about their structural integrity and their overall condition. Composite Overwrapped Pressure Vessels (COPVs) used for storing hydrogen gases at very high pressures are an example of a critical infrastructure that could benefit significantly from SHM. This can be used to increase the periodic inspection intervals, ensure safe operating conditions by early detection of anomalies, and ultimately estimate the remaining lifetime of COPVs. Therefore, in the Digital Quality Infrastructure Initiative (QI-Digital) in Germany, an SHM system is being developed for COPVs used in a hydrogen refueling station. In this study, the results of a lifetime fatigue test on a Type IV COPV subjected to many thousands of load cycles under different temperatures and pressures are presented to demonstrate the strengths and challenges associated with such an SHM system. During the cyclic testing up to the final material failure of the COPV, a sensor network of fifteen surface-mounted piezoelectric (PZT) wafers was used to collect the UGW data. However, the pressure variations, the aging process of the COPV, the environmental parameters, and possible damages simultaneously have an impact on the recorded signals. This issue and the lack of labeled data make signal processing and analysis even more demanding. Thus, in this study, semi-supervised, and unsupervised deep learning approaches are utilized to separate the influence of different variables on the UGW data with the final aim of detecting and localizing the damage before critical failure.

Economic Impact Assessment of Structural Health Monitoring Systems on the Lifecycle of a Helicopter Blade

Structural Health Monitoring Systems (SHMS) are currently widely investigated in the literature, however, their application to the aerospace industry is still limited. One of the reasons lies in the lack of methods aimed at evaluating their economic impact on the lifecycle of the structural element to be monitored [1]. In this work the economic impact of FBG-based SHMS was evaluated on a composite helicopter rotor blade, considering two perspectives: Beginning Of Life (BOL), concerning the blade production, and Middle Of Life (MOL), concerning its usage. The processes were modeled using the IDEF0 methodology, which allowed deriving the cost models for two scenarios: the current one, and the one including the SHMS. In BOL, the SHMS was supposed providing support to the development of new composite rotor blades, including the curing cycle definition and certification tests. In MOL the SHMS was supposed performing automatic scheduled inspections coupled with the traditional ones. The cost model was implemented in the Probabilistic Damage Tolerance Analysis [2] to compare the scenarios having the same blade Probability Of Failure and considering the performances of the SHMS in terms of Probability Of Detection and Probability of False Alarm. Results show that an economic benefit may be achieved using the SHMS in the development of new blades, potentially reducing the number of blades tested and number of autoclave cycles. Moreover, it was found that the traditional scheduled detailed inspection intervals could be extended if automatic SHMS inspections are performed in addition, maintaining the same Probability Of Failure. [1] Büchter, K. D. et al. (2022). Modeling of an aircraft structural health monitoring sensor network for operational impact assessment. Structural Health Monitoring, 21(1), 208-224. [2] Lin, K. Y., & Styuart, A. V. (2007). Probabilistic approach to damage tolerance design of aircraft composite structures. Journal of Aircraft, 44(4), 1309-1317.

Optimal Preventive Maintenance Scheduling of Multi State System. A Comparative Study of Different Meta-heuristic Algorithms

This work presents a comparative study of different meta-heuristic algorithms which are commonly used in the literature, such as Genetic Algorithms, Particle Swarm Optimization, Artificial Bee Colony and Grey Wolf Optimizer. These algorithms are applied to solve a multi-state system maintenance optimization problem considering time and system availability constraints. The objective is to find the optimal inspection and maintenance intervals for each component of the system in order to minimize the preventive maintenance cost of overall the system. The performances of the algorithms are compared using different metrics, regarding the quality and stability of the results and the speed of convergence, in order to determine the most efficient algorithms.

An analytical approach to evaluate life-cycle cost of deteriorating pipelines

Optimal inspection and maintenance planning for deteriorating pipelines is important for cost-effective risk management decisions. The objective of this study is to develop an analytical approach for determining the expected value and standard deviation of life-cycle cost (LCC) for deteriorating pipelines, which can incorporate imperfect repair and inspection actions without making independent assumptions of failure and repair events. The framework is developed based on a decision tree model by using analytical methods to evaluate events and considers the impact of the inspection schedules and possible repair actions on the probability distributions of failure times. To illustrate the proposed framework, a steel transmission pipeline with a corrosion defect is used, where a stochastic model of corrosion growth is assumed and burst failure is considered as the pipeline failure mode. The case study indicates that the failure cost due to two or more failure occurrences can be ignored; and the inspection interval, repair threshold and type, the length of lifetime considered, and unit failure cost all play important roles in the expected value of the total cost. The variation in the LCC estimation is found to be significant, and it has a great impact on determination of optimal decision variables.

A retrospective case-control study of non-compliances preceding foodborne outbreaks originating from food service premises

More than half of the foodborne outbreaks originate from food service premises (FSPs). Recognizing non-compliances observed during food control inspections and their association with outbreaks is important so that new measures to prevent outbreaks can be developed.We conducted a retrospective case-control study to compare food control inspection grades between outbreak and non-outbreak FSPs. A total of 105 outbreaks meeting the study inclusion criteria were identified from among 337 outbreak investigation reports in Finland in 2015–2021. Four randomized matched non-outbreak control FSPs were selected for each outbreak FSP. Inspection grades were compared using a paired t-test or a Wilcoxon signed-rank test. In addition, we studied the association between the time from the preceding inspection to the point of the outbreak and the grade of the preceding inspection. Furthermore, inspectors’ verbal descriptions of the inspection findings were analyzed by comparing n-gram incidences between outbreak and control FSPs.When outbreaks with the weakest strength of evidence (on a 4-level scale) were excluded and only the inspection preceding the outbreak was considered, the inspection grades for outbreak FSPs regarding the monitoring of employees’ health status were inferior compared to the control FSPs (p = 0.02). When comparing the grades of all available inspections prior to the outbreak, there was a difference not only in monitoring employees' health status (p = 0.04), but also in the verification of hygiene proficiency (p = 0.02). Non-compliance in the verification of hygiene proficiency might imply deficiencies in food safety knowledge, which can result in many types of non-compliance.We also demonstrated that the closer the preceding inspection was to the outbreak, the worse the inspection grades were. This implies that the observed non-compliances may lead to outbreaks in a short time if not corrected, and that a prolonged inspection interval might result in critical non-compliances in FSPs that have previously received satisfactory inspection grades. These results can be used to develop food control focus points and inspection frequency.

Some Comments Concerning the Preparation of and Fatigue Testing of the Aircraft’s Cable-Control System

Abstract The currently accepted rules that are applied to the aircraft cable-control systems’ operational use are based on the reactive maintenance idea and the comparative tests, inspections, and diagnostics performed at the mandatory intervals. Fatigue tests of the aviation cables are commonly conducted by bending in the range of ± 90° with constant load. Aircraft cable-control systems are subject to a number of random loads and deformations. Additionally, forces and their values are modified by the wear and tear of cable-pulley raceways, elastic deformations, and changes caused by temperature. The actual values of tension of aviation cable-control systems are relatively low, and bending usually does not exceed the maximum of ± 35°. Moreover, the forces characteristic of the control cables are nonlinear functions of the control surface deflection. This means that the typical fatigue tests we employ help with only comparative estimations and acceptance tests. It is not possible to estimate the operational durability of the systems and forecast inspections and diagnoses intervals based on the mentioned results. The present article utilizes the operational profiles of selected aircraft categories to determine the stochastic load-related deflection spectra for the preparation of cable fatigue-testing programs. Operation profiles are built considering a group of aircraft belonging to the same category, performing similar missions, for example, training missions, photogrammetric missions, aircraft towing, e.q., and having a similar share in the total resource. The special stands for the selected cable fatigue tests have been proposed. The cable test stand ensures the real stochastic loads for the cable use and other actual conditions of load. The proposed stand enables the simultaneous testing of more than one cable at different deformation parameters, for example, wrap angles. The results of the proposed method and tests can be used to estimate the operational durability of aviation-control systems as well as for inspection and diagnosis intervals as well.