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Abstract During actual operation, a steel wire rope is deformed due to stress interaction between its internal steel wires. In this article a finite element approach is employed to simulate the interaction of steel wire under practical working conditions. In addition, according to the amount of fatigue broken wire under experimental conditions, corresponding parameters are put forward to estimate the service life of steel rope. In this article, the effect of various conditions on service life of steel wire rope is analyzed, and approaches are put forward to improve its service life. The results show that the total stress in straight strand is larger than side strand stress, while the stress increases from inside to outside while increasing from inside to outside. The bigger the pulley diameter, the more steel wire breaks inside the wire rope. [ABSTRACT FROM AUTHOR]

Multivariable stochastic degradation system (MSDS) is quite common in industries such as blast furnace ironmaking, vehicle transportation, and aerospace manufacturing. Large‐scale complex equipments may be affected by multiple factors, resulting in not just a single deteriorating performance characteristic. It is difficult to handle unknown failure structures of practical systems by using traditional univariate degradation modeling methods. A novel health index (HI) is constructed to quantitatively analyze the health state for the overall system. Considering the interaction between internal reactions and external environments, the fractional Brownian motion (FBM), a typical non‐Markovian diffusion process, is added for the purpose of reflecting stochastic uncertainties and memory effects. Based on the wavelet estimators and the maximum likelihood estimation (MLE) algorithm, multi‐sensor observations of degradation variables are analyzed simultaneously to identify model parameters. A closed‐form distribution of system‐level remaining useful life (RUL) is obtained with a mild two‐layer approximation. Relevant case studies are then handled that adequately demonstrate the effectiveness and the practical utility of the proposed method. [ABSTRACT FROM AUTHOR]

Accurate remaining useful life (RUL) prediction is of great importance for predictive maintenance. With the recent advancements in sensor technology and artificial intelligence, the data-driven approaches to RUL prediction of industrial equipment have gained a lot of attention. However, past researches have not adequately considered the variety of degradation rates and the accumulated information in degradation processes. To deal with this problem, a novel two-stage machine learning approach of RUL prediction is proposed in this paper. A set of nonlinear health indicator functions are constructed to guide the training process of a long short-term memory learner of degradation processes, then a time delay neural network is utilized for RUL prediction. The superiority of the proposed approach in terms of prediction accuracy and conservativeness is demonstrated by a case study of rolling element bearing dataset. • Health indicator functions are constructed by considering various degradation rates. • The health indicator is utilized to guide the prediction modeling. • Long short-term memory learners are established to achieve the time scale alignment. • The time delay mechanism is introduced to fuse past feature information. • The approach has an advantage in accuracy and conservativeness. [ABSTRACT FROM AUTHOR]

It is widely noted in the literature that the degradation should be simplified into a memoryless Markovian process for the purpose of predicting the remaining useful life (RUL). However, there actually exists the long-term dependency in the degradation processes of some industrial systems, including electromechanical equipments, oil tankers, and large blast furnaces. This implies the new degradation state depends not only on the current state, but also on the historical states. Such dynamic systems cannot be accurately described by traditional Markovian models. Here we present an improved non-Markovian degradation model with both the long-term dependency and the item-to-item uncertainty. As a typical non-stationary process with dependent increments, fractional Brownian motion (FBM) is utilized to simulate the fractal diffusion of practical degradations. The uncertainty among multiple items can be represented by a random variable of the drift. Based on this model, the unknown parameters are estimated through the maximum likelihood (ML) algorithm, while a closed-form solution to the RUL distribution is further derived using a weak convergence theorem. The practicability of the proposed model is fully verified by two real-world examples. The results demonstrate that the proposed method can effectively reduce the prediction error. [ABSTRACT FROM AUTHOR]

Some practical systems such as blast furnaces and turbofan engines have degradation processes with memory effects. The term of memory effects implies that the future states of the degradation processes depend on both the current state and the past states because of the interaction with environments. However, most works generally used a memoryless Markovian process to model the degradation processes. To characterize the memory effects in practical systems, we develop a new type of degradation model, in which the diffusion is represented as a fractional Brownian motion (FBM). FBM is actually a special non-Markovian process with long-term dependencies. Based on the monitored data, a Monte Carlo method is used to predict the remaining useful life (RUL). The unknown parameters in the proposed model can be estimated by the maximum likelihood algorithm, and then the distribution of the RUL is predicted. The effectiveness of the proposed model is fully verified by a numerical example and a practical case study. [ABSTRACT FROM PUBLISHER]

A four stages twelfth algebraic order symmetric two-step method with vanished phase-lag and its first and second derivative is developed in this paper for the first time in the literature. More specifically in this paper the following will be investigated: (i) the phase-lag analysis for the development of the new method, (ii) the construction of the new method, (iii) the local truncation error analysis which is based on the radial Schrödinger equation, (iv) the interval of periodicity analysis and the stability analysis which is based on a scalar test equation with frequency not equal with the frequency of the scalar test equation used for the phase-lag analysis, (v) the error estimation procedure based on the algebraic order, (vi) the numerical results from our numerical tests for the study of the efficiency of the new obtained method. The numerical tests are based on the numerical solution of the Schrödinger equation. [ABSTRACT FROM AUTHOR]

A prerequisite for the existing remaining useful life prediction methods based on stochastic processes is the assumption of independent increments. However, this is in sharp contrast to some practical systems including batteries and blast furnace walls, in which the degradation processes have the property of long-range dependence. Based on the fractional Brownian motion, we adopt a degradation process with long-range dependence to predict the remaining useful life of the above systems. Because the degradation process with long-range dependence is neither a Markovian process nor a semimartingale, the exact analytical first passage time is difficult to derive directly. To address this problem, a weak convergence theorem is first adopted to approximately transform a fractional Brownian motion-based degradation process into a Brownian motion-based one with a time-varying coefficient. Then, with a space-time transformation, the first passage time of the degradation process with long-range dependence can be obtained in a closed form. Unknown parameters in the degradation model can be identified using discrete dyadic wavelet transform and maximum likelihood estimation. Numerical simulations and a practical example of a blast furnace wall are given to verify the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Bacteria in rural sewage collection systems have the important influences on operation and maintenance risks, such as sedimentation blockage and harmful gas accumulation, and pollutant pre-treatment ability. It is necessary to analyze and interpret the influence on bacterial communities caused by the location (sewage, biofilms, and deposits), season (winter and spring, summer and autumn), and system type (sewers and ditches) to better understand the bacterial characteristics in rural sewage collection systems. To achieve the above purpose, 96 samples obtained from practical rural sewage collection systems in eight villages were analyzed by 16S rRNA whole region sequencing methods. The results indicate that locations and seasons caused significant influences on the overall bacterial communities, which were mainly affected by temperature, sewage quality and bacterial survival preference, and 13 genera of sulfate-reducing bacteria (SRB), 2 genera of ammonia-oxidizing bacteria (AOB), 2 genera of nitrite-oxidizing bacteria (NOB), and 9 genera of water-related pathogenic bacteria (WPB) were detected in rural sewage collection systems. SRB, AOB, NOB, and WPB tended to inhabit in biofilms or deposits rather than in sewage. The total relative abundance of SRB in summer and autumn (∼2.19%) was higher than in winter and spring (∼0.41%), and the WPB distribution in different seasons showed significant distinction. Additionally, some of SRB, AOB, NOB, and WPB also showed significant differences in sewers and ditches. Overall, this study provided a deeper understanding of bacteria in rural sewage collection systems and could further provide the basic parameter for the operation and maintenance risk control. • Locations and seasons caused significant influences on bacterial communities. • SRB, AOB, NOB, and WPB tended to inhabit in biofilms or deposits. • Total abundance of SRB in summer and autumn was higher than in winter and spring. • Some of SRB, AOB, NOB, and WPB showed significant differences in sewers and ditches. [ABSTRACT FROM AUTHOR]

Highlights • The aluminium foam sandwich was fabricated using friction stir welding. • No undesirable interface reaction existed among the face sheets and the foam core. • AFS prepared through FSW possessed high flexural strength and impact resistance. • The AFS possessed good sound absorption and insulation performance. Abstract The aluminium foam sandwich (AFS), prepared through the traditional adhesive or brazing methods, usually exhibits a low flexural strength due to the poor combination among the face sheets and the aluminium foam core. In this work, a new method, named friction stir welding (FSW), was utilized to fabricate AFS. The results demonstrated that the joining of the face sheets and aluminium foam was achieved through the plasticized metal flow. In addition, the AFS prepared through FSW possessed high flexural strength and impact resistance, as well as good sound absorption and reduction performance. This demonstrated that the FSW was a good method to prepare AFS. [ABSTRACT FROM AUTHOR]

For data-driven remaining useful life (RUL) prediction, an appropriate degradation model is critically important to achieve accurate prediction. The degradation processes in some practical systems are not only related to the age but also related to the current degradation state, and the degradation processes may be non-Markovian processes. However, most existing stochastic process-based degradation models only depend on the age, and simply assume that the increments are independent. In this paper, an age- and state-dependent degradation model with long-range dependence is developed, which is more general than most of the existing models based on either Brownian motions (BMs) or fractional Brownian motions (FBMs). The Radon-Nikodym derivative is utilized to obtain a likelihood ratio function of unknown parameters, and the estimates are obtained by maximizing the likelihood ratio function. A weak convergence theorem is introduced to approximate the FBM by a BM with a time-varying coefficient. A time-space transformation is further utilized to obtain an approximate explicit solution of the RUL. At last, numerical simulations and two real case studies of blast furnace walls and ball bearings are adopted to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Considering the application requirements of independent imaging payloads design, a novel scheme of separated position and orientation system (POS) is proposed, in which the high-precision inertial sensors of traditional centralized POS fixed on the imaging payloads are mounted on three gimbals of the inertially stabilized platform (ISP), respectively, and make them integrated. Then, the kinematics model of the ISP system is built to transmit the inertial information measured by separated inertial sensors mounted on ISP gimbals and flight body to the imaging payloads, calculating the position and attitude of the imaging payloads to achieve the function of separated POS. Based on the model, a series of simulations indicate that the precision difference between separated system and centralized system is ignorable under the condition of angular motion and variable velocity motion. Besides the effective function equal to traditional centralized system, the separated POS enhances the integration with the ISP. Moreover, it improves the design independence of the imaging payloads significantly. [ABSTRACT FROM AUTHOR]