Reliability Of Mechanical Systems Research Articles

Can system reliability be predicted from average component reliabilities?

The paper reveals that a prediction of system reliability on demand based on average reliabilities on demand of components is a fundamentally flawed approach. A physical interpretation of algebraic inequalities demonstrated that assuming average component reliabilities on demand entails an overestimation of the system reliability on demand for systems with components logically arranged in series and series-parallel and underestimation of the reliability on demand for systems with components logically arranged in parallel. The key reason for these discrepancies is the variability of components from the same type. Techniques for countering variability by promoting asymmetric response through inversion have also been introduced. The paper demonstrates that variability during assembly operations can affect negatively the reliability of mechanical systems. Accordingly, techniques for reducing the variability of stresses during assembly operations have been discussed. Finally, the paper provides a discussion related to the reasons for the relatively slow adoption of domain-independent methods for improving reliability despite their numerous advantages.

Domain-invariant feature fusion networks for semi-supervised generalization fault diagnosis

Machinery fault diagnosis based on deep learning methods is cost-effective to guarantee safety and reliability of mechanical systems. Due to the variability of machinery working condition and difficulty of data obtaining under different health states, it is desirable to enhance the generalization capability to unseen working conditions for the fault diagnosis models trained by available data sets under limited number of working conditions. Considering that labeling industrial data is also a laborious work, this paper proposes a novel semi-supervised domain generalization model, termed domain-invariant feature fusion networks (DIFFN) for intelligent fault diagnosis under unseen target working conditions. The main contributions are that, intra-domain-invariant features are considered to capture the intrinsic semantic information within the domain and are fused with inter-domain-invariant features to enhance the discrimination and generalization abilities in fault diagnosis. First, a domain-invariant representation learning method is established to learn the inter- and intra-domain-invariant features using two network branches and fuse them via a fusion module. Second, a mutual learning strategy is designed to enable the network branches and the fusion module to learn from each other, thereby improving the discrimination of the extracted features for accurate fault diagnosis. Lastly, a feature divergence maximization strategy is embedded between the two network branches to improve the generalization ability of the fault diagnosis model. Experiments on two bearing data sets demonstrate that the proposed model has better diagnostic accuracy and stability over state-of-the-art semi-supervised domain generalization methods, indicating its great potential for application in generalization fault diagnosis of machinery under unseen target working conditions.

Influence of post-processing milling conditions on the machinability and residual stresses evolution of LPBF 18Ni300 maraging steel

Metal additive manufacturing (MAM) currently allows the production of mechanical components with technical specifications suitable for structural applications with a high level of complexity. Despite the most recent technological developments, additively manufactured parts may still lack the geometrical and dimensional accuracy as well as surface integrity required for precision mechanical assemblies and system reliability. These requirements often lead to post-processing operations through precision machining technologies. The present work focuses on the machinability study of 18Ni300 maraging steel obtained by laser powder bed fusion and its comparison with the conventional counterpart. Milling tests were carried out covering a wide range of cutting parameters, aiming at understanding their influence and comparing the obtained results in terms of cutting force, specific cutting pressure, roughness and chip morphology. In depth residual stresses have been measured for different operational and metallurgical conditions and their comparison was performed. A more significant effect of the feed parameter on the analysed data is noticed, particularly regarding the affected layer depth of the residual stresses due to cutting. Moreover, the higher mechanical strength of the additively manufactured alloy does not translate into an equivalent increase in the required average specific cutting pressure.

Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing

Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety, and economical efficiency of mechanical systems. Traditional data-driven fault diagnosis methods require obtaining a dataset of full failure modes in advance as the training data. However, this kind of dataset is not always available in some critical industrial scenarios, which impairs the practicability of the data-driven fault diagnosis methods for various applications. A digital twin, which establishes a virtual representation of a physical entity to mirror its operating conditions, would make fault diagnosis of rolling bearings feasible when the fault data are insufficient. In this paper, we propose a novel digital twin-driven approach for implementing fault diagnosis of rolling bearings with insufficient training data. First, a dynamics-based virtual representation of rolling bearings is built to generate simulated data. Then, a Transformer-based network is developed to learn the knowledge of the simulated data for diagnostics. Meanwhile, a selective adversarial strategy is introduced to achieve cross-domain feature alignments in scenarios where the health conditions of the measured data are unknown. To this end, this study proposes a digital twin-driven fault diagnosis framework by using labeled simulated data and unlabeled measured data. The experimental results show that the proposed method can obtain high diagnostic performance when the real-world data is unlabeled and has unknown health conditions, proving that the proposed method has significant benefits for the health management of critical rolling bearings.

A CNN‐BiLSTM‐Bootstrap integrated method for remaining useful life prediction of rolling bearings

AbstractRolling bearings, an essential fundamental component in machinery and equipment, have been widely used. Predicting the remaining useful life (RUL) of rolling bearings helps maintain the reliability of mechanical systems. Accurate prediction of RUL requires extracting deep features in complex non‐linear vibration signals, the prediction results often vary widely. This paper proposes a RUL prediction method based on convolutional neural network (CNN), bi‐directional long‐short term memory (BiLSTM), and bootstrap method (CNN‐BiLSTM‐Bootstrap) to model the forecasting uncertainty. The first step is to extract the first prediction time (FPT) of the degradation phase of rolling bearings using an adaptive method for the 3σ intervals of rolling bearing vibration signal kurtosis. The model extracts the spatio‐temporal features through CNN and BiLSTM, and combines the bootstrap method to quantify the RUL prediction interval (PI) of rolling bearings. The comparison with existing models verified the effectiveness and generalization of the proposed model.

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

This research introduces a challenge in integrating and cleaning the data, which is a crucial task in object matching. While the object is detected and then measured, the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage, abnormal stopping, and disaster. Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement. To solve all these problems, this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image. A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation (UCC) algorithm. The vibration measurement is calculated using the Finite-Difference Algorithm (FDA), a machine vision system fitted with a macro lens sensor that is capable of capturing the image at a closer range, which does not affect the quality of displacement measurement from the video frames. The field test was conducted on the TAFE (Tractors and Farm Equipment Limited) tractor parts, and the percentage of error was recorded between 30% and 50% at very low vibration values close to zero, whereas it was recorded between 5% and 10% error in most high-accelerations, the essential range for vibration analysis. Finally, the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges. The use of a macro lens enables to capture of image frames at very close-ups. A 30% to 50% error percentage has been reported when the vibration amplitude is very small. Therefore, this study is not suitable for Nano vibration analysis.

Зносостійкість титанового сплаву ВТ1-0 з модифікованою поверхнею в умовах абразивного впливу

The cost of rebuilding machine parts as a result of wear is enormous and rising every year. At a US symposium on reducing wear in machinery, the general consensus was that wear management is central to solving national problems such as energy conservation, material reduction, and ensuring the reliability and safety of mechanical systems. Nitriding significantly increases the wear resistance of metals and alloys. The formation of chemical compounds in titanium alloys by introducing nitrogen or increasing its concentration limit changes the rate of chemical reactions and the kinetics of oxide film growth, and increases their adhesion to the substrate. This leads to a decrease in the intensity of adhesive node formation and improves the tribological characteristics of titanium alloys. Therefore, it is necessary to study a titanium alloy with a hardened nitrided layer to obtain experimental results to determine the regularity of the influence of diffusion saturation parameters on wear resistance under abrasive conditions. The universal installation "VIPA-1" was used for vacuum ion nitriding in the pulse mode and the formation of diffusion layers on the surface. Technological parameters of vacuum ion nitriding in the pulse mode: temperature - 550°C, pressure - 25-150 Pa, processing time - 10 hours, ratio of reaction gases - 80% Ag + 20% N2. For experimental studies, samples of VT1-0 titanium alloy with dimensions of 30x30 mm and a thickness of 5 mm were used. The surface hardening of the samples was carried out uniformly around the entire perimeter, which ensures a uniform thickness of the diffusion layer. The analysis of the operating conditions of the friction surfaces of most structural elements made it possible to use the installation for bench tests according to the scheme of wear with a free abrasive (Brinell method). Wear tests were carried out on an experimental setup in accordance with GOST 23.208-79 (Fig. 2). The friction process was modelled in the presence of a free, not rigidly fixed abrasive, which coincides with the American standard ASTM C 6568. The experiment was carried out at a sliding speed of 0.158 m/s, a load of 20 kg (with a shoulder of 272 mm) and a friction path of 50 m. Steel 45, hardened to a hardness of 480-500 HB, was used as a reference. The wear body was a disc made of VT1-0 titanium alloy with a diameter of 100 mm and a thickness of 3.5 mm. On the basis of experimental studies, it has been established that the main mechanisms for increasing the wear resistance of titanium alloys as a result of the process of diffusion saturation of the surface with nitrogen during vacuum ion nitriding in the pulse mode are strengthening of the surface layers; creation of a favourable residual stress pattern; change in the patterns of deformation of the surface layers; change in the chemical and adhesive properties of the surface; transfer of diffusing nitrogen atoms into the depth of the matrix during friction due to the tribodiffusion effect. Conclusions. 1. Maximum wear rate of titanium alloy VT1-0 without hardening. 2. The effect of thermocyclic nitriding increases the wear resistance of VT1-0 alloy: in sand - 3 times; in water + sand - 3.5 times; in salt + sand - 2.5 times. 3. The effect of isothermal nitriding increases the wear resistance of VT1-0 alloy: in sand - 4 times; in water + sand - 3.5 times; in salt + sand - 2.5 times.

Reliability prediction of a mechatronic hydraulic drive at the early design stages

The article deals with the reliability of technological machines that use hydraulic feed drives. A priority task in mechanical engineering is the design of mechanical systems with higher stability, reliability, and performance. Various mechatronic systems are used to solve this problem, including mechatronic motion modules for hydraulic drives of technological machines. It is noted that the study of the reliability of mechatronic systems presents a special problem since the interaction of mechanical, hydraulic, and electronic systems gives rise to some new aspects of the theory of reliability. The main technical solutions for reliability incorporated in the design directly impact the machine's functional and economic characteristics. When predicting reliability at an early stage of design, there is the greatest uncertainty (entropy) in assessing the possible states of the machine. As an object of study, a mechatronic hydraulic drive is considered an electro-hydraulic motion module, which can be used in feed drives for heavy metal-cutting machines or industrial robots with a large load capacity. An important parametric characteristic of such a drive is the positioning accuracy of the working body, its stability, and the preservation of values within the specified limits over time. A review of the methodology for assessing and predicting the reliability of mechanical systems is carried out. It is noted that several statistical methods require the accumulation of test results for serial models or prototypes, but many important factors may not be taken into account. The purpose of this article is to obtain the results of predicting the parametric reliability of mechatronic hydraulic drives by using the method of expert assessments (rank correlation) at the early stages of design. This method is based on the ability of independent experts (qualified experts in the field) to provide useful information in the face of quantitative uncertainty. When setting the problem of predicting reliability, the factors that affect the positioning accuracy of the hydraulic drive were ranked in order of importance (ranked). An analytical relationship was established between the weight of the factor and its number in the series. The arithmetic mean weight, the mean relative weight, the standard deviation of the factors that affect the parametric reliability of the drive, and the coefficient of variation are determined. The consistency of expert opinions was shown based on heuristic indicators using the concordance coefficient (Kendall criterion). The considered technique can be used to predict and evaluate the reliability of mechatronic systems that are being developed for use in various fields of technology.

Modeling the Failure Rate of a Standby Multi-Component System and Improving Reliability

Evaluating the reliability of a system requires knowledge of the failure modes to which it is subject. Complex topology systems generally require a high level of availability, which is a function of the arrangement of elements (components) in the system. To avoid serious failures for such complex systems, recourse can be had to the redundancy techniques available in the literature. These techniques help improve system reliability, without affecting the reliability of system components. This paper is interested in the proposal of a model for evaluating the failure rate of a standby multi-component system and in improving the reliability of mechanical systems, arranged in a topology (series, parallel, or mixed).

Prediction of rolling contact fatigue crack propagation in bearing steels using experimental crack growth data and linear elastic fracture mechanics

Rolling contact fatigue (RCF) is a major life limiting factor for machine elements that employ non-conformal, rolling sliding, lubricated contacts such as rolling bearings and gears. This paper explores the application of linear elastic fracture mechanics (LEFM) principles, as commonly used in structural fatigue, for prediction of RCF crack propagation. A triple-disc contact fatigue machine is used to generate RCF cracks of varying lengths in AISI 52100 bearing steel roller specimens. Crack propagation rates across the surface are measured using optical inspection of test specimens and the final crack geometry is established through specimen sectioning. A numerical finite element model of surface breaking RCF cracks based on LEFM methods is devised to predict the evolution of stress intensity factors (SIFs) during over-rolling of the contact over the experimentally observed crack geometries. The model employs a suitable fracture mechanics mesh to resolve stresses at the crack tip and accounts for Hertzian contact stresses, contact friction and crack face friction. Potential effects of lubricant pressurisation within the crack are not modelled. The predicted SIFs are then related to the experimentally measured crack propagation rates to establish the applicability of the LEFM principles to RCF crack propagation. Results show that LEFM can be used to predict the growth of surface braking RCF cracks. For cracks longer than about 100 μm, a Paris law relationship with the stress intensity exponent of about 4 is derived. Mode II was seen to be the dominant mode of propagation for surface braking RCF cracks. Mode I SIFs are much smaller but can exhibit significant values when the contact is located just ahead of the crack mouth. Decreasing the crack face friction significantly increases mode II stress intensity suggesting that this is one important mechanism by which lubricant entry into the surface braking crack can accelerate its propagation. The findings can help in improving the reliability of mechanical systems by supporting the development of new tools for prediction of remaining useful life of machine components such as bearings and gears.

A mechanical system reliability degradation analysis and remaining life estimation method——With the example of an aircraft hatch lock mechanism

The failure mechanism and degradation analysis of an aircraft cabin door lock mechanism are studied. A data-driven degradation analysis is conducted based on the characteristics of two types of degradation data for the lock mechanism. The degradation process of the fluctuating elevation is described by Wiener process, and the lifetime is described using the Inverse Gauss (IG) process. The correlation between the two degradation processes is studied, and the correlation analysis is carried out with four copula functions. Moreover, the optimal model is obtained for reliability analysis using three model selection methods and Bayesian inference based on the Markov chain Monte Carlo (MCMC) method for parameter estimation. Finally, a residual life prediction model is proposed, and the accuracy of the model is tested with the error. The results show that the RUL prediction accuracy can be accepted, which helps to improve the reliability and service life of a type of aircraft lock mechanism.

An Analysis of the Load Distribution Characteristics of a Cylindrical Roller Bearing Including the Component Deformation and Waviness

Abstract Cylindrical roller bearings (CRBs) are key components of mechanical transmission systems. The internal load distribution and stiffness (LDAS) have significant effects on the bearing fatigue life and vibrations. It can also affect the precision, reliability, and service life of mechanical transmission systems. Due to the external loads applied on the bearing, the flexible deformations of rings and rollers would be produced, which will change the LDAS. To study the LDAS of CRB, an improved quasi-static model of CRB is established. The flexible deformations and waviness of rings and rollers are considered. An analytical calculation method based on the Newton–Raphson method is conducted to obtain the LDAS. The results show that the radial force, speed, and waviness can greatly affect the LDAS; the flexible deformations of rings and rollers increase with the increments of radial force and speed; the internal load distribution is also greatly affected by the waviness, which leads to the uneven load distribution; the radial stiffness decreases with the increment of waviness amplitude; and the waviness order has a slight effect on the radial stiffness.

An advanced mixed-degree cubature formula for reliability analysis

Efficient assessment of mechanical system reliability subject to arbitrary probability distributions and dependent input parameters signifies an important yet challenging task. To tackle this problem, this study proposes a new moment-based method for reliability analysis of complex mechanical systems which incorporates the advanced mixed-degree cubature formula and vine copula function. To start with, a method integrating the vine copula function and Rosenblatt transformation is developed for transferring the uncertainty with dependent random variables, in which the vine copula is applied for depicting the correlation between random variables. An advanced mixed-degree cubature formula is then established for calculating statistical moments of performance function, which enables to capture sufficient uncertainty information of variables after rotating integral node. The Hermite polynomial model is adopted here to reconstruct the probability distribution of performance function with the statistical moments. To demonstrate the effectiveness of the proposed method, four illustrative examples are implemented in this study, in which Monte Carlo Simulation (MCS) and dimension-reduction techniques, are performed for comparisons. The results show that the proposed method can handle multi-dimensional correlation effectively and well balance computational accuracy and efficiency for both statistical moments and probability distribution evaluations.

A Novel Classification Method to Random Samples for Efficient Reliability Sensitivity Analysis

Abstract Reliability sensitivity analysis is important to measure how uncertainties influence the reliability of mechanical systems. This study aims to propose an efficient computational method for reliability sensitivity analysis with high accuracy and efficiency. In this study, coordinates of some points on the limit state function are first calculated through Levenberg–Marquardt (LM) iterative algorithm, and the partial derivative of system response relative to uncertain variables is obtained. The coordinate mapping relation and the partial derivative mapping relation are then established by radial basis function neural network (RBFNN) according to these points calculated by the LM iterative algorithm. Following that, the failure samples can be screened out from the Monte Carlo simulation (MCS) sample set by the well-established mapping relations. Finally, the reliability sensitivity is calculated by these failure samples and kernel function, and the failure probability can be obtained correspondingly. Two benchmark examples and an application of industrial robot are used to demonstrate the effectiveness of the proposed method.

Multi-scale deep neural network approach with attention mechanism for remaining useful life estimation

Prognostics and Health Management (PHM) is the core task in modern industries to provide the reliability and availability of mechanical systems. In recent years, the degradation behaviors have been extensively employed to estimate the remaining useful life in PMH technologies. In this research, a novel data-driven framework based on the multi-scale network structure, called MCA-BGRU, is proposed to provide the remaining useful life prediction, which combines multi-scale convolution neural network (CNN), bidirectional gated recurrent unit (BGRU), multi-head self-attention (MHSA) mechanism, and fully-connected layers. In this proposed structure, multi-scale CNN blocks and the MHSA mechanism are constructed to capture high-level representations from the multivariate input data automatically. Then, a BGRU layer is leveraged to learn various temporal tendencies between extracted features. Additionally, particle swarm optimization is adopted to simultaneously tune the hyperparameters of this framework. The superiority of the MCA-BGRU is validated by the well-known C-MAPSS dataset of NASA. The experimental results revealed that the presented approach achieves an improvement of 0.32% and 5.6% in terms of RMSE and Score values compared with the various existing studies.

Integrated framework for estimating remaining useful lifetime through a deep neural network

This paper proposes an integrated framework for a deep neural network to estimate the remaining useful life (RUL) to ensure the reliability and safety of complex mechanical systems and enable proactive maintenance for intelligent operation. This data-driven method can predict complex and highly nonlinear degradation characteristics that are difficult to predict using physics-based prognostics and health management. In particular, this study focused on feature preprocessing and hyperparameter optimization, whereas previous studies had focused on the neural network architecture to improve prediction accuracy and robustness. The proposed integrated framework comprises four phases: feature preprocessing, feature reasoning using a deep neural network, hyperparameter optimization using a genetic algorithm, and RUL estimation. In the first phase, sensor measurements sensitive to degradation are selected and separated into primary and dynamic degradation trends. In addition, step differential values are extracted to account for multiple operational modes using an unsupervised clustering method. In the second phase, feature reasoning is performed using a deep neural network to characterize hidden complex and highly nonlinear degradation features. The health indicators manipulated in the first phase are trained using the proposed deep neural network. In the third phase, a genetic algorithm is introduced to optimize the hyperparameters used in feature preprocessing and reasoning. The final phase estimates the RUL using the proposed deep neural network with optimized hyperparameters. The proposed method was validated on the C-MAPSS dataset. The results show that the proposed integrated framework outperformed other state-of-the-art machine learning and deep learning methods under different operational conditions, suggesting that efficient feature preprocessing and hyperparameter optimization significantly improve the prediction accuracy and robustness of RUL for data-driven prognostics and health management.

Dynamic Analysis of Surface-Mounted Permanent Magnet Type Coaxial Magnetic Gear With Damper Bar Considering Magnetic Field Modulation Effect

Coaxial Magnetic Gear (CMG) has unstable dynamic characteristics by hunting or pull-out action of the output rotor when the load or speed are changed, unlike traditional mechanical gears. These dynamic characteristics need to be improved in order to secure the reliability of mechanical power transmission system by fast and accurate response. In this paper, the damper bar used in synchronous machines is considered as a method to improve the dynamic characteristics of CMG. The Surface-mounted Permanent Magnet (SPM) type CMG is selected as the analysis model. The space harmonics of the magnetic flux density of stationary and rotary members of CMG namely, modulating pieces, inner and outer rotor, are analyzed and characterized the influence of them on the improvement of dynamic characteristics as well as torque reduction. Also, the magnetic flux density characteristics and the damping effect are compared according to the position of the damper bars on two rotors and the modulating pieces. In conclusion, the considerations about the perspective for design and application are presented when using the damper bar for SPM type CMG.

A Novel Approach of Label Construction for Predicting Remaining Useful Life of Machinery

Rolling bearings are key components of rotating machinery, and predicting the remaining useful life (RUL) is of great significance in practical industrial scenarios and is being increasingly studied. A precise and reliable remaining useful life prediction result provides valuable information for decision-makers, which is essential to ensure the safety and reliability of mechanical systems. Generally, the RUL label is considered to be an ideal life curve, which is the benchmark for RUL prediction. However, the existing label construction methods make more use of expert experience and seldom mine knowledge from data and combine experience to assist in constructing a health index (HI). In this paper, a novel and simple approach of label construction is proposed for predicting the RUL accurately. More specifically, the degradation index of the multiscale frequency domain is first extracted. Furthermore, the fuzzy C-means (FCM) algorithm is innovatively used to divide the degradation data into several stages to obtain the turning point of degradation. Then, a nonlinear degradation index, the RUL label with the turning point, was constructed based on principal component analysis (PCA). Finally, the recurrent neural network (RNN) is used for prediction and verification. In order to verify the effectiveness of the proposed approach, two different bearing lifecycle datasets are gathered and analyzed. The analysis result confirms that the proposed method is able to achieve a better performance, which outperforms some existing methods.

Tribological behavior of grooves textured thrust cylindrical roller bearings under dry wear

To prolong the serve life of roller element bearings (REB) and improve the reliability of mechanical system, the tribological behavior of the “washers-cage-rollers” system of grooves textured thrust cylindrical roller bearings (TCRB, 81107TN) under dry wear was researched. The pattern parameters include: width of grooves (WOG, 50, 100, and 150 μm), depth of grooves (DPOG, 7, 11, and 15 μm), as well as groove deflection angle (GDA, 0°, 45°, 90°, and 135°). The influence mechanism of grooves on the tribological properties of REBs is discussed. The results show that: As the GDA is 45°, the coefficient of friction (COF) and mass loss of bearing is the lowest among four angles. The average COFs of grooves textured bearings are much higher than that of smooth one, and their mass losses are all lower than that of smooth bearing. The influence of pattern parameters on the surface stresses of grooves textured bearings is weak. In this work, when the WOG is 50 μm and the DPOG is 7 μm, the wear loss of grooves textured bearing is the lowest, reduced by up to 75.6%. This work can provide a valuable reference for the raceway design and optimization of REBs.

Исследование надежности судовых насосов по данным технического наблюдения

The article describes the analysis of the ship systems’ reliability at the stage of the technical design, which is based on the reliability data of individual system elements and mathematical methods of reliability theory. During the ship operation maintenance deficiencies can lead to the equipment failure, which can be prevented by the methods of instrumental diagnostics of system elements, maintenance repairs regardless of the current technical condition, and classification inspection during the ship operation. Collecting the detailed data on changes in the functional reliability of ship mechanical systems, which could confirm or deny the advantages of each of the existing methods is not enough. There has been carried out collecting, processing and analyzing the actual data on the life cycles of various pumps of ship systems at different stages of service life, as well as beyond the designated service life or operating time. There are examined the general ship systems and auxiliary power units of icebreakers and floating structures. The examined equipment has standard pumps with different capacity and efficiency. Service life, assigned resource (operating time), frequency of repair are taken as the criteria of pump reliability and durability. There have been shown the actual life cycles of pumps in dependence of the service life in years on the operating time in hours; in addition, data on the passage of maintenance repairs, maintenance, inspection of the technical condition, as well as replacement or extension of the service life are plotted on the graphs. It has been inferred that some of the pumps in the ship's systems should be replaced before the full resource was developed, while the performance of the systems as a whole was preserved or restored as a result of repairs; some pumps have exceeded their specified resource, but during the technical inspection their operation was extended, the operability of the systems was also preserved.