Reliability Of Mechanical Systems Research Articles

Digital twin-driven attention-guided convolutional networks for intelligent fault diagnosis across different domains

Fault diagnosis of rolling bearings is crucial in industries to ensure the reliability, safety, and economic feasibility of mechanical systems. Conventional data-driven methods for fault diagnosis depend on pre-existing datasets containing various failure modes for training. However, obtaining such datasets can be challenging, especially in critical industrial environments, hindering the applicability of these methods across different scenarios. To address this challenge, this research introduces a novel approach called a digital twin-driven attention-guided convolutional network (DTACN) for bearing fault diagnostics with limited data. This study offers two significant enhancements: (1) Development of an intricate digital twin model for bearings, which includes multi-scale Kinetic simulations of the bearing’s operational parameters. This model utilizes the architectural characteristics of the bearing and the severity of the failure to predict the vibratory system’s response accurately; and (2) Establishment of a domain adaptation-based framework employing deep convolutional models and attention mechanism to transfer knowledge from simulated signals to real signals. This framework facilitates efficient fault recognition even in scenarios with limited prior knowledge. The effectiveness of the proposed approach has been rigorously validated through comprehensive experiments.

Fault diagnosis of high-speed motorized spindles based on lumped parameter model and enhanced transfer learning

Condition monitoring of rotating components is crucial for ensuring the reliability and safety of mechanical systems, and artificial intelligence (AI) plays a significant role in achieving the goal. However, the high costs and complexity associated with components like high-speed motorized spindles present significant challenges in collecting complete fault samples. Therefore, we propose a new approach to tackle the challenges. The process commences with establishing a lumped parameter dynamic model for the high-speed motorized spindle. Then, the parameters such as stiffness, eccentricity, and damping in the lumped parameter model were optimized using genetic algorithm. Subsequently, simulated fault samples are acquired by introducing excitation to normal simulated signals. Finally, transfer learning techniques are utilized for intelligent fault diagnosis. The training set consists of simulated fault samples, while the testing set comprises experimental fault samples. Our approach aims to enhance the efficiency and accuracy of fault diagnosis for high-speed motorized spindles while also addressing the challenge of high cost.

ReF-DDPM: A novel DDPM-based data augmentation method for imbalanced rolling bearing fault diagnosis

Effective bearing fault diagnosis is crucial to ensure the safety and reliability of mechanical systems. Due to the complex and harsh working environment, mechanical data often comes from imbalanced datasets, which is a pressing problem in diagnosis applications. However, currently proposed data augmentation methods mainly based on generative adversarial networks, remain challenging in balancing the quality and diversity of the generation samples. To solve it, this paper proposes a new data enhancement method called the reparameterized residual denoising diffusion probability model (ReF-DDPM) and applies it to fault diagnosis. The proposed architecture includes a forward diffusion process and a reverse denoising process, where Gaussian noise and original samples are transformed by Markov chains. To improve the quality of generation samples, the noise prediction network is modified for better feature representation by enhancing intra-level and inter-level features. Furthermore, signal labels are added to the model as conditional information to direct the generation of relevant category samples during the sampling process. The study provides a new data augmentation method for bearing imbalanced data, and generation data can be further used for fault diagnosis tasks. Verification experiments demonstrate the effectiveness and good generalization of the method, and improve the accuracy of imbalance fault diagnosis.

Investigation of pad radius effects on multiaxial fretting fatigue behavior of al2024 alloy

Fretting fatigue, a critical phenomenon prevalent in engineering applications, occurs at the interface of contacting surfaces under cyclic loading, presenting significant challenges. The advent of Finite Element Analysis (FEA) has transformed the investigation of fretting fatigue by offering detailed insights into stress distributions and fatigue damage mechanisms. This study delves into the impact of pad radius on the fretting fatigue behavior of Al2024 alloy using FEA, with a focus on hot spot analysis and the Smith-Watson-Topper (SWT) criterion. The analysis aims to validate numerical results against analytical findings and explore the role of pad radius in determining contact behavior, fatigue life, and hot spot locations. By incorporating "fe-safe" software, computations are streamlined, facilitating efficient comparison of different fatigue initiation criteria. The results demonstrate that variations in pad radius significantly influence fretting fatigue behavior. Larger pad radii tend to distribute stresses more uniformly across the contact interface, resulting in reduced fatigue damage and longer fatigue life compared to smaller pad radii. Hot spot analysis reveals that smaller pad radii concentrate stresses at specific regions, accelerating fatigue damage initiation. The findings contribute to a deeper understanding of fretting fatigue mechanisms, shedding light on the importance of pad radius in designing more durable engineering components. Through professional scientific methodology, this study provides valuable insights into optimizing pad design to mitigate fretting fatigue and enhance the reliability of mechanical systems.

Multimodal imbalanced‐data fault diagnosis method based on a dual‐branch interactive fusion network

AbstractBearing‐fault diagnosis in rotating machinery is essential for ensuring the safety and reliability of mechanical systems. However, under complicated working conditions, the number of normal mechanical equipment samples can far exceed the number of faulty ones. When the data are so imbalanced, data fault diagnosis cannot be easily conducted using conventional deep learning methods. This study proposes a fault diagnosis method based on a dual‐branch interactive fusion network, which improves the accuracy and stability of bearing‐fault diagnosis. First, a dual‐branch feature representation network comprising an iterative attention‐feature fusion residual neural network and a long short‐term memory network is designed for extracting different modal features. Meanwhile, intermodal fusion of the extracted features is performed through multilayer perception. Based on the cost‐sensitive regularization loss, a new joint loss function is then designed for network training. Finally, the effectiveness of the proposed method is verified through comparative experiments, visualization analyses, ablation experiments, and generalization performance experiments.

Review of Vibration Analysis and Structural Optimization Research for Rotating Blades

AbstractBlades are important parts of rotating machinery such as marine gas turbines and wind turbines, which are exposed to harsh environments during mechanical operations, including centrifugal loads, aerodynamic forces, or high temperatures. These demanding working conditions considerably influence the dynamic performance of blades. Therefore, because of the challenges posed by blades in complex working environments, in-depth research and optimization are necessary to ensure that blades can operate safely and efficiently, thus guaranteeing the reliability and performance of mechanical systems. Focusing on the vibration analysis of blades in rotating machinery, this paper conducts a comprehensive literature review on the research advancements in vibration modeling and structural optimization of blades under complex operational conditions. First, the paper outlines the development of several modeling theories for rotating blades, including one-dimensional beam theory, two-dimensional plate–shell theory, and three-dimensional solid theory. Second, the research progress in the vibrational analysis of blades under aerodynamic loads, thermal environments, and crack factors is separately discussed. Finally, the developments in rotating blade structural optimization are presented from material optimization and shape optimization perspectives. The methodology and theory of analyzing and optimizing blade vibration characteristics under multifactorial operating conditions are comprehensively outlined, aiming to assist future researchers in proposing more effective and practical approaches for the vibration analysis and optimization of blades.

Cyclic Wear Reliability of 2D Monolayers.

Understanding wear, a critical factor impacting the reliability of mechanical systems, is vital for nano-, meso-, and macroscale applications. Due to the complex nature of nanoscale wear, the behavior of nanomaterials such as two-dimensional materials under cyclic wear and their surface damage mechanism is yet unexplored. In this study, we used atomic force microscopy coupled with molecular dynamic simulations to statistically examine the cyclic wear behavior of monolayer graphene, MoS2, and WSe2. We show that graphene displays exceptional durability and lasts over 3000 cycles at 85% of the applied critical normal load before failure, while MoS2 and WSe2 last only 500 cycles on average. Moreover, graphene undergoes catastrophic failure as a result of stress concentration induced by local out-of-plane deformation. In contrast, MoS2 and WSe2 exhibit intermittent failure, characterized by damage initiation at the edge of the wear track and subsequent propagation throughout the entire contact area. In addition to direct implications for MEMS and NEMS industries, this work can also enable the optimization of the use of 2D materials as lubricant additives on a macroscopic level.

ВЫБОР ПАРАМЕТРОВ ТЕХНИЧЕСКОГО СОСТОЯНИЯ ЭЛЕМЕНТОВ ПОГЛОЩАЮЩЕГО АППАРАТА, ОПРЕДЕЛЯЮЩИХ ЕГО БЕЗОТКАЗНОСТЬ

The problems to plan and ensure a given level of reliability of mechanical systems at all stages of their design and operation occupy an important place in the problem of ensuring the quality of machine-building products. With a significant number of scientific methods and approaches to solving theoretical and applied issues of reliability theory, one of the key directions is to determine the reliability indicators of technical systems and their elements, in particular a wide range of products including sliding friction pairs with periodic impacts. The study of failure origins, from the point of view of physical-mechanical processes occurring in surface layers of elements, under the influence of external factors, allows selecting parameters characterizing the functional state of the pair, and, as a result, its reliability. For example, for sliding friction pairs with periodic impacts, such a parameter is the geometric dimensions of the conjugate elements. The decrease in the geometric dimensions of conjugate elements, due to wear, characterizes the rate of change of the initial parameter values to the maximum permissible ones determining the failure, and can be approximated by linear, power, exponential and other dependencies obtained from actual measurements. According to the obtained functional dependencies of parameter changes over operating time, it makes possible to determine the norm for the parameter regulating their pre-failure state, beyond which failure prediction is possible with varying probability, and, as a result, the reliability of the system as a whole. The use of this method to reliability forecasting through the determination of the pre-failure state of the system can be projected onto technical objects, which performance indicators are geometric parameters that ensure the performance, first of all, of the functions of the elements themselves and the entire technical system, and worn out during operation.

An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition.

Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary mechanical system. In practice, the sample proportion between faulty data and healthy data in rotating mechanical system is imbalanced. Furthermore, there are commonalities between the bearing fault detection, classification, and identification tasks. Based on these observations, this article proposes a novel integrated multitasking intelligent bearing fault diagnosis scheme with the aid of representation learning under imbalanced sample condition, which realizes bearing fault detection, classification, and unknown fault identification. Specifically, in the unsupervised condition, a bearing fault detection approach based on modified denoising autoencoder (DAE) with self-attention mechanism for bottleneck layer (MDAE-SAMB) is proposed in the integrated scheme, which only uses the healthy data for training. The self-attention mechanism is introduced into the neurons in the bottleneck layer, which can assign different weights to the neurons in the bottleneck layer. Moreover, the transfer learning based on representation learning is proposed for few-shot fault classification. Only a few fault samples are used for offline training, and high-accuracy online bearing fault classification is achieved. Finally, according to the known fault data, the unknown bearing faults can be effectively identified. A bearing dataset generated by rotor dynamics experiment rig (RDER) and a public bearing dataset demonstrates the applicability of the proposed integrated fault diagnosis scheme.

Fault diagnosis of rotating machinery using novel self-attention mechanism TCN with soft thresholding method

Rotating machinery (e.g. rolling bearings and gearboxes) is usually operated in high-risk and vulnerable environments such as time-varying loads and poor lubrication. Timely assessment of the operational status of rotating machinery is crucial to prevent damage caused by potential failure and shutdown, which significantly enhances the reliability of mechanical systems, prolongs the service life of critical components in rotating machinery, and minimizes unnecessary maintenance costs. In this regard, in this paper, a novel approach named self-attention mechanism combining time convolutional network with soft thresholding algorithm (SAM-TCN-ST) is proposed for fault intelligent recognition of rotating machinery. Specifically, the vibration signals are transformed into time-frequency graphs with distinct features utilizing the continuous wavelet transform, and then the proposed SAM-TCN-ST algorithm is employed for capturing essential data characteristics and classification performance. Eventually, datasets from rolling bearings and gearboxes are used to verify the accuracy and effectiveness of the proposed method compared with state-of-the-art benchmark networks such as pure TCN, convolutional neural networks and long short-term memory models. Experimental results demonstrate that the recognition accuracy rate of the proposed SAM-TCN-ST is higher than that obtained from the benchmark methods. This research presents an intelligent and viable solution for achieving real-time monitoring of the status and detecting faults in rotating machinery, thereby expectedly enhancing the reliability of mechanical systems. Consequently, the proposed SAM-TCN-ST algorithm holds significant potential for applications in prognostic and health management practices related to rotating machinery.

Investigation on the health evaluation of mechanical system in powertrain based on subjective and objective fusion method

The powertrain is a core component of military heavy tracked vehicles, and the accurate evaluation of its reliability and health is gradually becoming a key technology to improve our military combat effectiveness. However, the powertrain is a complex system integrating mechanical, electrical, and hydraulic components, which is difficult to achieve its accurate assessment and comprehensive characterization. Therefore, using data-driven and expert opinion fusion method to evaluate the mechanical system health is gradually becoming a research trend. This paper points out the composition of evaluation system, and puts forward how to calculate the index. Then using subjective and objective fusion method to analyze these indexes of mechanical system health and calculate the weights of each index. Based on the weights, the fuzzy comprehensive evaluation method is used to score and summarize the change law of mechanical system health. In conclusion, this study enriches the method of effectively evaluating the mechanical system health, and verifies the accuracy of this evaluation method. It will provide an evaluation basis for improving the mechanical system reliability.

A novel degradation model and reliability evaluation methodology based on two-phase feature extraction: An application to marine lubricating oil pump

The Wiener-process-based Degradation Approach with Two-phase Algorithm (W-DATA) is introduced as a multi-dimensional framework explicitly designed for modeling mechanical system reliability. This approach leverages an enhanced Empirical Mode Decomposition, known as P-EMD, for degradation feature extraction, and utilizes Approximate Entropy (ApEn) for quantifying feature complexities. W-DATA seamlessly integrates these components into a time-resolved degradation model. To enhance its robustness, the proposed reliability model synergizes with a Box-Cox transformed Wiener model, allowing for improved adaptability to non-Gaussian data landscapes. The paper outlines a strategy for establishing a reliable threshold for this data-driven degradation model based on non-Gaussian data, a critical aspect for reliability modeling using the Wiener process. This strategy addresses the challenge of Wiener process verification through normality and independent testing of data increments. The integrative methodology is recommended for application in machinery reliability analytics and has undergone rigorous validation through a 770 h real-world test on a marine lubricating oil pump reducer. The results affirm its efficacy and feasibility in accurately estimating mechanical system reliability.

Reliability Analysis and Optimization Method of a Mechanical System Based on the Response Surface Method and Sensitivity Analysis Method

Mechanical system reliability analysis constitutes a primary research focus in the field of engineering. This study aims to address the issue of complex mechanical systems with intricate mechanisms and nonlinear reliability equations that are challenging to solve. To this end, we present a reliability analysis and optimization methodology that merges the response surface and sensitivity analysis methods. A comprehensive formation of reliability assessment and optimization of complex mechanical systems is achieved by creating a response surface model to fit the complex state function and solving the reliability parameters, followed by an error sensitivity analysis to determine the mechanical system’s reliability adjustment strategy. Finally, these methods are applied to a cylindrical material transport device to preliminarily realize the reliability assessment and average reliability optimization goals. The study’s findings may offer a theoretical framework and research opportunities to evaluate and enhance the reliability of intricate mechanical systems.

Preface

Fourth Conference of Computational Methods & Ocean Technology (COTech 2023) November 30 – December 1, 2023, University of Stavanger, Stavanger, Norway The COTech (Computational Methods & Ocean Technology) conference started as part of the research and dissemination activities of the Program Area for research “COTech - Computational methods in Offshore Technology” at Faculty of Science and Technology, University of Stavanger (UiS) with its first event in 2017. This Program Area for Research was founded in 2015 with seven professors, four associate professors, two adjunct professors and five research (PhD) students from the Department of Mechanical and Structural Engineering and Materials Science (IMBM), whose expertise and competence lies primarily within use of computational methods such as finite element methods, boundary and volume element methods, computational fluid dynamics and the like in marine and subsea technology, marine operations, design and analysis of mechanical systems, integrity and reliability of offshore structures and mechanical systems, renewable energy and wind engineering. In the ocean-related engineering area in particular, numerical computation approach is nowadays not only serving as a means to cultivate and realize innovative ideas, but also it is becoming the primary choice to solve complex engineering problems for the harsh and unfriendly environment in the Arctic.Like the previous conference, this year’s COTech conference is conducted as part of the dissemination activities of the Institute Strategic Program (ISP) entitled “Computational Methods and Ocean Technology” established under the research activities of the Department of Mechanical and Structural Engineering and Materials Science. This three-year program (2021 - 2023) intends to bring researchers of the department under four selected research areas:1. Ocean Energy Conversion - installations & dynamic analysis of offshore wind turbines, structural health monitoring, corrosion assisted fatigue, ocean wave energy.2. Aquaculture Technology - efficient farming techniques, advanced numerical modeling and computation, fluid-structure interaction, design effective maintenance programs for aquaculture.3. Marine and Subsea Technology - sediment erosion (scour), scour prediction CFD models, structural integrity and fatigue life of offshore structures, adaptive control of ROVs in subsea application, predictive maintenance of subsea structures4. Innovative Solutions - 3D printing based innovative solutions, inspection drones, augmented reality for maintenance training,…In addition to the thematic areas covered in the last three conferences, the 4th COTech conference accommodated some of the emerging technologies and applications such as machine learning techniques, additive manufacturing technologies, 3D printing in healthcare technology, smart energy storage and bio-inspired design.In general, the conference is intended to provide a platform for academics and professionals working within diverse forms of the computational methods, Ocean/Offshore technology and Oil and Gas technologies to come together, present their recent works in the area, exchange ideas, and establish professional networks. It will serve as a forum for multidisciplinary research and bring together enable them to exchange their research experience and disseminate their results within the involved fields.List of Conference Organizing Committee, Volume Editors, Invited Keynote Speakers, Additional plenary session speakers, Technical Committee Members and Reviewers are available in this Pdf.

A Shape-constrained Transfer Temporal Transformer Network for remaining useful life prediction of rotating machines

Predicting remaining useful life (RUL) of rotating machines can promote predictive maintenance and improve the mechanical system reliability. This paper proposes a Shape-constrained Transfer Temporal Transformer Network (SC3TN) for RUL prediction across different rotating machines. The disentangled representation learning is utilized to extract invariant features across different domains. Health indexes are constructed with invariant features considering shape constraints and all previous health states. The operating time is encoded by a Temporal Encoding in predicting process. The SC3TN is tested on two tasks, namely RUL prediction across different working conditions and different equipment. Compared to state-of-the-art methods, SC3TN reduces the mean absolute error by 4.94% and 5.47% and improves the score by 8.5% and 20.83% on two tasks. The experiments show that the proposed method can achieve more accurate RUL prediction than existing methods, which demonstrates the potential of the proposed method for predicting RUL without collecting degradation data in advance.

Application of structural methods to assess the reliability of ship’s mechanical systems

At present, reliability indicators are of great importance at the design stages, during operation and in cases of extending the technical life. This is one of the reasons for choosing the research topic on the application of structural methods for evaluating the reliability of ship mechanical systems. The operational stage of the life cycle of ship’s mechanical systems is considered in the paper. Durability criteria such as service life, operating time, frequency of repairs are chosen as reliability indicators. In turn, work on the repair, maintenance or replacement of ship’s mechanical systems elements is carried out in accordance with the assigned regulations or in the case of an unforeseen failure. The comparison of event models of changes in the operating conditions of ship’s mechanical systems is carried out. To estimate the remaining resource in the form of graphs, events are shown by service life and operating hours, distributed by years. In order to improve the existing methods for assessing reliability, a set of elements of the average values of the residual resource and residual output is considered. The approach proposed by the authors for assessing both element by element and the entire ship mechanical system as a whole allows us to consider the residual resource from the moment of monitoring its technical condition to the transition to the limit state, to plan technical measures and prevent possible malfunctions. Three different time slices models corresponding to planned repairs and maintenance of systems, replacement of system elements and unforeseen accidents are considered in the study. The difference in the amplitudes of the calculated indicators of the ship’s mechanical systems reliability has shown that the closer the options between the maximum and minimum values are to the average, the more accurately the statistical indicators characterize this pattern. However, amplitude deviations can be used to apply fault risk assessment. This line of research seems to be important for ships in the Arctic navigation area, which are characterized by increased requirements for ship survivability.

Influence of a transmission oil degradation on physico-chemical properties and tribological performance

Lubricant degradation can have a significant impact on the efficiency and reliability of mechanical systems. This study analyses the effects of field operation on the physicochemical properties and tribological performances of gear transmission oils. Viscosity and composition of fresh-new oils and field-collected ones are analysed and tribological tests on the High Frequency Reciprocating Rig (HFRR) and the Mini Traction Machine (MTM) are carried out, to identify potential differences. Results reveal changes in intrinsic properties and tribological behaviour. They show that the evolution of the oil is highly dependent on the system and operating conditions, results that differ from those traditionally observed with engine oils.

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.