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This study presents a 3D pick-rock contact calculation method for conical picks, aiming to develop a predictive method with high accuracy and lithological tolerance for peak cutting force (PCF). The method is based on the projection profile method and D. L. Sikarskie stress distribution function. By integrating Griffith’s theory with rock damage constitutive model, the energy relationship between the rock fracturing process and crack propagation process is analyzed. Furthermore, in order to accurately correct the PCF, the energy correction function (C-Kf) is proposed to calculate the damage intensity index (Ke), which accounts for the relationship between rock brittleness and rock damage elastic–plastic energy. To validate the method, it is compared with full-scale cutting tests and three existing models, and statistical analysis confirms its high lithological tolerance and accuracy, the present model has the highest R2 of 0.90404, which is at least 12.5% higher relative to the mainstream models. Moreover, incorporating Ke into the method further enhances its predictive capability. [ABSTRACT FROM AUTHOR]

Urban road traffic is one of the primary sources of carbon emissions. Previous studies have demonstrated the close relationship between traffic flow characteristics and carbon emissions (CO2). However, the impact of dynamic traffic distribution on carbon emissions is rarely empirically studied on the network level. To fill this gap, this study proposes a dynamic network carbon emissions estimation method. The network-level traffic emissions are estimated by combining macroscopic emission models and recent advances in dynamic network traffic flow modeling, namely, Macroscopic Fundamental Diagram. The impact of traffic distribution and the penetration of battery electric vehicles on total network emissions are further investigated using the Monte Carlo method. The results indicate the substantial effect of network traffic distribution on carbon emissions. Using the urban expressway network in Ningbo as an example, in the scenario of 100% internal combustion engine vehicles, increasing the standard deviation of link-level traffic density from 0 to 15 veh/km-ln can result in an 8.9% network capacity drop and a 15.5% reduction in network carbon emissions. This effect can be moderated as the penetration rate of battery electric vehicles increases. Based on the empirical and simulating evidence, different expressway pollution management strategies can be implemented, such as petrol vehicle restrictions, ramp metering, congestion pricing, and perimeter control strategies. [ABSTRACT FROM AUTHOR]

This article presents a metal matrix composite material consisting of NiTi wires embedded in nitrile butadiene rubber (NBR) that preserves NBR's inherent acoustic characteristics while enabling acoustic modification through the NiTi phase transition induced by stress and temperature. The macroscopic mechanical parameters of transversely isotropic NiTi-NBR composite materials are derived by means of a secondary bridging model that takes into account interfacial phases. On this basis, the acoustic impedance properties and absorption coefficient of composite materials were examined as a function of NiTi volume fraction using the transfer matrix method. The accuracy and effectiveness of the theoretical method were verified by comparing the calculated results with finite element simulation. The research results indicated that regulating the volume fraction of NiTi can lead to the anticipated value of the input impedance of composite materials, improving impedance matching with media like water and rubber, which offers novel insights and a theoretical foundation for the development of underwater sound-absorbing materials. [ABSTRACT FROM AUTHOR]

For conducting scientific research at depths in the ocean, deep-sea probes are essential pieces of equipment. The cylindrical shell is the most sensible and rational packaging structure for these detectors. New technical challenges for enhancing the pressure resistance and lightweight design of the pressure-resistant cylindrical shell arise from the need to ensure that the detector packaging structure can withstand the immense water pressure at tens of thousands of meters in the underwater environment, while simultaneously reducing the detector packaging structure's self-weight. This article examines the detection system's deep-sea pressure-resistant cylindrical shell. To address the issue of the pressure-resistant shell's insufficient ability to counteract the overall instability caused by the inability to form unstable half-waves in the radial direction when the ring rib pressure-resistant shell experiences it, a design method for the ribs inside the unequal-stiffness pressure-resistant cylindrical shell is suggested. The shell's instability pressure increases by 9.65 MPa following the stiffness adjustment. Simultaneously, in order to attain even more lightweight optimization, the optimal inner rib section was obtained by applying the orthogonal topology optimization method, which also reduced the weight by 106.8 g and effectively improved the compression stability of the high-pressure cylindrical shell structure. Based on this, key optimization variables were found by performing sensitivity analysis on the cylindrical shell structure's parameters. Then, with lightweighting as the primary objective, the high-pressure-resistant cylindrical shell's optimal structural parameters were found using a multi-objective optimization process using the second-generation fast non-dominated genetic algorithm (NSGA-2). This resulted in a weight reduction of 1.2492 kg, or 17.26% of the original pressure-resistant shell. This has led to the development of a lightweight, highly pressure-resistant method for packaging marine exploration equipment structures. [ABSTRACT FROM AUTHOR]

The constitutive model and modulus parameter equivalence of shape memory alloy composites (SMAC) serve as the foundation for the structural dynamic modeling of composite materials, which has a direct impact on the dynamic characteristics and modeling accuracy of SMAC. This article proposes a homogenization method for SMA composites considering interfacial phases, models the interface stress transfer of three-phase cylinders physically, and derives the axial and shear stresses of SMA fiber phase, interfacial phase, and matrix phase mathematically. The homogenization method and stress expression were then used to determine the macroscopic effective modulus of SMAC as well as the stress characteristics of the fiber phase and interface phase of SMA. The findings demonstrate the significance of volume fraction and tensile pre-strain in stress transfer between the fiber phase and interface phase at high temperatures. The maximum axial stress in the fiber phase is 705.05 MPa when the SMA is fully austenitic and the pre-strain increases to 5%. At 10% volume fraction of SMA, the fiber phase's maximum axial stress can reach 1000 MPa. Ultimately, an experimental verification of the theoretical calculation method's accuracy for the effective modulus of SMAC lays the groundwork for the dynamic modeling of SMAC structures. [ABSTRACT FROM AUTHOR]

Clarifying the noise source and the contribution of each path is essential for the system's noise control. The auxiliary converter cabinet, which is a crucial component of rail transportation, has numerous intricate noise sources. The contribution of each path point must be inverted-solved using known transfer functions and target point test values when identifying noise sources. This article suggests a method for diagnosing noise using transfer path analysis and neural networks (TPA-NN). Firstly, the principle and scheme for analyzing the transmission path of the converter cabinet are proposed. The transfer function of each path is obtained by selecting suitable path points, reference points, and target points for air and structure acoustic vibration experiments. The external target point data are then combined with the neural network's linear fitting function, and the contribution of each path is used as an output for network training while some path point contributions are rebuilt. The results indicate that the method's outcomes are most accurate when the converter cabinet's path point is 13 and the target point is 6. This approach offers an innovative technique for locating noise sources in intricate systems. [ABSTRACT FROM AUTHOR]

Variations with respect to perspective, lighting, weather, and interference from dynamic objects may all have an impact on the accuracy of the entire system during autonomous positioning and during the navigation of mobile visual simultaneous localization and mapping (SLAM) robots. As it is an essential element of visual SLAM systems, loop closure detection plays a vital role in eradicating front-end-induced accumulated errors and guaranteeing the map's general consistency. Presently, deep-learning-based loop closure detection techniques place more emphasis on enhancing the robustness of image descriptors while neglecting similarity calculations or the connections within the internal regions of the image. In response to this issue, this article proposes a loop closure detection method based on similarity differences between image blocks. Firstly, image descriptors are extracted using a lightweight convolutional neural network (CNN) model with effective loop closure detection. Subsequently, the image pairs with the greatest degree of similarity are evenly divided into blocks, and the level of similarity among the blocks is used to recalculate the degree of the overall similarity of the image pairs. The block similarity calculation module can effectively reduce the similarity of incorrect loop closure image pairs, which makes it easier to identify the correct loopback. Finally, the approach proposed in this article is compared with loop closure detection methods based on four distinct CNN models with a recall rate of 100% accuracy; said approach performs significantly superiorly. The application of the block similarity calculation module proposed in this article to the aforementioned four CNN models can increase the recall rate's accuracy to 100%; this proves that the proposed method can successfully improve the loop closure detection effect, and the similarity calculation module in the algorithm has a certain degree of universality. [ABSTRACT FROM AUTHOR]

In this paper, we propose an ultrasonically coupled mechanical rock-breaking technology, creatively design an ultrasonically coupled mechanical rock-breaking drum, concurrently develop an ultrasonic cracking simulation method based on test coordination, and study the cracking mechanism and characteristics of ultrasonically pre-broken rock in order to increase the rock-breaking efficiency of shearer drums and lengthen pickaxe service life. To further understand the theory behind ultrasonic-coupled mechanical rock breaking, the operation of a fusion drum and the implications of ultrasonic field theory in a solid medium are first examined. Second, the impact and mechanism of the ultrasonic pre-crushing of the target red sandstone are investigated in conjunction with conducting a rock uniaxial compression test and RFPA2D modeling. Furthermore, an ultrasonic pre-crushing fracturing mechanism test of the target red sandstone further reveals the effect and mechanism of ultrasonic fracturing. The efficacy of ultrasonic-coupled mechanical single-cutter cutting is then investigated using the discrete element cutting model (PFC2D) of red sandstone. The results show that under the action of ultrasonic waves with an excitation frequency of 41 kHz, cracks can effectively be produced inside the rock mass of the target red sandstone, and the cumulative amount of acoustic emission is as high as 513, which reduces the strength of the rock mass and disintegrates its internal structure; the average cut-off force of the purely mechanical rock-breaking mode is 6374 N, and that of ultrasonically coupled rock breaking is 4185 N, which is a reduction of 34.34%, and can be attributed to the fact that ultrasonic waves can loosen the structure of the rock mass. This is explained by the ability of ultrasonic vibrations to weaken the structure of rock. The coupled rock-breaking technology not only simplifies mechanical cutting and rock breaking but the lower force can also reduce a pick-shaped trunnion's wear failure cycle. This improves the environment for subsequent pick-shaped trunnion cutting and rock breaking and prevents the pick-shaped trunnion from being subjected to high-stress loads for an extended period of time so as to prolong its working life. [ABSTRACT FROM AUTHOR]

To address the problem that anechoic coatings frequently fall off from modern submersible hulls and are detrimental to the realization of underwater acoustic stealth, this paper focuses on the broadband sound absorbing of acoustic coverings in debonding states from fully bonded span to fully shedded conditions. Based on the non-uniform waveguide theory, subdomain splitting approach, and wave propagation theory in layered media, a global transfer matrix method (TMM) is developed for predicting the sound absorption of a composite overburden with periodic cavities in all peeling situations. Meanwhile, the corresponding acoustic-structure fully coupled finite element (FE) simulation and hydroacoustic impedance tube-based absorption experiment are sequentially performed for the lining in a semi-bonded state to comprehensively verify the accuracy and reliability of the present analytical methodology. Then, the influence laws of debonding states, material properties, and geometric parameters on the global absorption performance are investigated in depth to reveal the multiple energy dissipation mechanisms. The results show that the shedding state primarily affects the sound absorption characteristics of anechoic coatings in the low- to mid-frequency band below 7 kHz. [ABSTRACT FROM AUTHOR]

Featured Application: The methodology proposed in this paper studies the mobility of a multi-loop spherical mechanism with a spherical slider containing a spherical prismatic pair. By analyzing the motion of each closed kinematic chain that the mechanism consists of, not only can the proposed methodology thoroughly and accurately identify the mechanism's singularity configurations, but the method can also demonstrate the mechanism's range of motion. Therefore, the proposed method ultimately identifies the mechanism's motion continuity and smoothness, which are two of the most essential characteristics a multi-loop spherical mechanism possesses. Hence, the proposed methodology can help mechanism designers create multi-loop mechanisms with spherical sliders without motion deficiencies by avoiding the mechanisms' singularity configurations. Multi-loop spherical mechanisms are extremely beneficial for creating versatile mechanical devices, including robotic joints and surgical tools, since multi-loop spherical mechanisms possess unique capabilities to operate in spatial situations with relatively simple movement. Nevertheless, the research on multi-loop spherical mechanisms with spherical sliders containing spherical prismatic pairs is lacking. Therefore, the main innovation of this paper is to propose the Stephenson-III two-loop spherical mechanism that possesses a spherical slider containing a spherical prismatic pair and to analyze the proposed spherical mechanism's motion characteristics. An algebraic approach was employed to obtain the branch graphs of the proposed spherical mechanism with a spherical slider. The branch graphs were categorized into two types, according to whether branch points existed. With the algebraic approach, loop equations of the two spherical kinematic chains inside the proposed spherical mechanism were established to identify the input–output curves and singularity curves, with which the branch graphs were obtained. With the branch graphs, the joint rotation spaces (JRSs) of the proposed mechanism were recognized and so were the dead center positions, branches, sub-branches, and branch points. The results from the mathematical analysis were simulated and verified by three-dimensional (3D) models of the proposed spherical mechanism. The analytical results demonstrate that the spherical prismatic pair diversifies the motion of the proposed spherical mechanism by producing rotational sliding movement, which can cover the entire circumference of a specific greater circle on the proposed mechanism's sphere. [ABSTRACT FROM AUTHOR]

Product performance, function, cost, and the level of module generalization are all significantly influenced by product modular design, but different goods require different division indicators and techniques. The purpose of this study is to provide a set of appropriate modular division techniques for FDM 3D printers. This research offers an ecologically friendly module division index and uses module clustering as the module division principle in accordance with the current industrial development trend and the fundamental requirements of FDM 3D printer consumers in the current market. The K-means algorithm is used to use the Jaccard similarity coefficient as the metric of similarity of the DSM clustering process to realize the module division of the FDM 3D printer after studying the function–behavior–structure mapping model of the 3D printer. Additionally, the elbow method–cluster error variance and average contour coefficient evaluation systems were built, respectively, in order to verify the viability of the FDM 3D printer module division method and obtain the best module division results. By analyzing these two systems, it was discovered that when the FDM 3D printer was divided into three modules, the in-cluster error variance diagram obviously had an inflection point, and the average profile coefficient and other modular approaches that need to be adjusted to their respective goods can use this division method as a theoretical foundation and point of reference. [ABSTRACT FROM AUTHOR]

Credit card fraud leads to billions of losses in online transaction. Many corporations like Alibaba, Amazon and Paypal invest billions of dollars to build a safe transaction system. There are some studies in this area having tried to use machine learning or data mining to solve these problems. This paper proposed our fraud detection system for e- commerce merchant. Unlike many other works, this system combines manual and automatic classifications. This paper can inspire researchers and engineers to design and deploy online transaction systems.

Active control of structural modal vibration is an effective strategy to enhance the sound quality of cabs in commercial vehicles. However, accurate determination of the positioning and quantity of modal active control sensors and actuators is crucial for cabs with intricate structures, owing to the presence of multiorder modes and their coupling. The study presented herein focuses on the cab of a commercial vehicle and contemplates the features of the irregular large-space structure of the cab. By capitalizing on the modal frequency and mode shape of the cab, utilizing the piezoelectric control principle and modal vibration energy as the assessment index, an advanced multimode composite control criterion is postulated to ascertain the configuration of primary sensors and actuators. The particle swarm optimization (PSO) objective function is constructed to accomplish the optimal position matching of the actuator/sensor, using the multimodal surface velocity vector of the vibration sensor as the core parameter. Furthermore, an improved linear adaptive particle swarm optimization (LA-PSO) technique is advanced to satisfy the requirements of optimal convergence performance and accuracy of the complex cab structure. The optimization culminates in a 9 × 9 multichannel active control scheme for determining the optimal position of the actuators/sensors. This investigation provides a technical foundation for the active control of sound quality in automotive cabs and presents an innovative method for implementing effective noise control systems in large-scale machinery and equipment. [ABSTRACT FROM AUTHOR]

Orthogonal antisymmetric composite laminates embedded with shape memory alloys (SMAs) wires have the potential to improve the sound quality of vibro-acoustics by taking advantage of the special superelasticity, temperature phase transition, and pre-strain characteristics of SMAs. In this research, space discretion and mode decoupling were employed to establish a vibro-acoustic sound quality model of SMA composite laminates. The association between the structural material parameters of SMA composite laminates and the sound quality index is then approached through methodologies. Numerical analysis was implemented to discuss the effects of SMA tensile pre-strain, SMA volume fraction, and the ratio of resin-to-graphite in the matrix on the vibro-acoustic sound quality of SMA composite laminates within a temperature environment. Subsequently, the sound quality test for SMA composite laminates is thus completed. The theoretically predicted value appears to agree well with the experimental outcomes, which validates the accuracy and applicability of the dynamic modeling theory and method for the sound quality of SMA composite laminates. The results indicate that attempting to alter the SMA tensile pre-strain, SMA volume fraction, and matrix material ratio can be used to modify loudness, sharpness, and roughness, which provides new ideas and a theoretical foundation for the design of composite laminates with decent sound quality. [ABSTRACT FROM AUTHOR]

The performance of near-field acoustic holography (NAH) with a sparse sampling rate will be affected by spatial aliasing or inverse ill-posed equations. Through a 3D convolution neural network (CNN) and stacked autoencoder framework (CSA), the data-driven CSA-NAH method can solve this problem by utilizing the information from data in each dimension. In this paper, the cylindrical translation window (CTW) is introduced to truncate and roll out the cylindrical image to compensate for the loss of circumferential features at the truncation edge. Combined with the CSA-NAH method, a cylindrical NAH method based on stacked 3D-CNN layers (CS3C) for sparse sampling is proposed, and its feasibility is verified numerically. In addition, the planar NAH method based on the Paulis–Gerchberg extrapolation interpolation algorithm (PGa) is introduced into the cylindrical coordinate system, and compared with the proposed method. The results show that, under the same conditions, the reconstruction error rate of the CS3C-NAH method is reduced by nearly 50%, and the effect is significant. [ABSTRACT FROM AUTHOR]

Two algorithms that are distinct from the closed algorithm are proposed to create the inverse kinematics model of the UR10 robot: the Sequential Quadratic Programming (SQP) algorithm and the Back Propagation-Sequential Quadratic Programming (BP-SQP) algorithm. The SQP algorithm is an iterative algorithm in which the fundamental tenet is that the joint's total rotation radian should be at a minimum when the industrial robot reaches the target attitude. With this tenet, the SQP algorithm establishes the inverse kinematics model of the robot. Since the SQP algorithm is overly reliant on the initial values, deviations occur easily and the solution speed, and the accuracy of the algorithm is undermined. To assuage this disadvantage of the SQP algorithm, a BP-SQP algorithm incorporating a neural network is introduced to optimize the initial values. The results show that the SQP algorithm is an iterative algorithm that relies excessively on the initial values and has a narrow range of applications. The BP-SQP algorithm eliminates the limitations of the SQP algorithm, and the time complexity of the BP-SQP algorithm is greatly reduced. Subsequently, the effectiveness of the SQP algorithm and the BP-SQP algorithm is verified. The results show that the SQP and BP-SQP algorithms can significantly reduce the operation time compared with the closed algorithm, and the BP-SQP algorithm is faster but requires a certain number of samples as a prerequisite. [ABSTRACT FROM AUTHOR]

In this paper, we consider a redundancy allocation problem in a series‐parallel system with uncertain component lifetimes of multiple states, which involves multitype components and mixed redundancy strategy. We develop a distributionally robust redundancy allocation model using state‐dependent ambiguity set, which describes the mean, expected cross‐deviation, and the support conditional on each state of the component lifetime distribution. Our structural analysis of the worst‐case system reliability function identifies a concave equivalent for the conditional worst‐case reliability function in each state and achieves an averaged finite piece‐wise affine representation for the overall worst‐case system reliability function, which enjoys valuable operational insights for reliability evaluation and scalable optimization of the redundancy designs. Furthermore, we analyze the reliability guarantees for the worst‐case reliability in optimality under possible misspecification of ambiguity‐set parameters, which admit the structure of "Reliability Target + Target Surplus + Estimated Elasticity," and insightfully imply that the auxiliary dual variables obtained via solving the design problem can be regarded as the scale of elasticity. Computationally, the redundancy design problem can be linearized and reformulated as a mixed 0–1 second‐order cone program. Exploiting the above finite piece‐wise affine structure of the worst‐case reliability function, the design problem also admits a practical iterative decomposition scheme with finite convergence, which is more scalable especially for the possible situations of large number of states in practice. Finally, numerical experiments including a case with real‐life data of braking system components well justify the value of state information incorporated by our model in producing favorable cost‐effective redundancy designs for reliability operations. [ABSTRACT FROM AUTHOR]

Fillet welds of highly reflective materials are common in industrial production. It is a great challenge to accurately locate the fillet welds of highly reflective materials. Therefore, this paper proposes a fillet weld identification and location method that can overcome the negative effects of high reflectivity. The proposed method is based on improving the semantic segmentation performance of the DeeplabV3+ network for structural light and reflective noise, and, with MobilnetV2, replaces the main trunk network to improve the detection efficiency of the model. To solve the problem of the irregular and discontinuous shapes of the structural light skeleton extracted by traditional methods, an improved closing operation using dilation in a combined Zhang-suen algorithm was proposed for structural light skeleton extraction. Then, a three-dimensional reconstruction as a mathematical model of the system was established to obtain the coordinates of the weld feature points and the welding-torch angle. Finally, many experiments on highly reflective stainless steel fillet welds were carried out. The experimental results show that the average detection errors of the system in the Y-axis and Z-axis are 0.3347 mm and 0.3135 mm, respectively, and the average detection error of the welding torch angle is 0.1836° in the test of a stainless steel irregular fillet weld. The method is robust, universal, and accurate for highly reflective irregular fillet welds. [ABSTRACT FROM AUTHOR]

Green and highly selective synthesis of organonitrogen chemicals (ONCs) using the renewable energy source biomass over noble-metal free solid catalysts under common room temperature and pressure conditions is still a major challenge. Here, we report a sustainable electrochemical method for selective synthesis of several valuable ONCs with high yields using biomass-derived furanic aldehydes over greenly fabricated TiS2 nanosheets through a facile synthesis. Based on a range of characterization techniques including high-resolution transmission electron microscopy and X-ray absorption fine structure, a well-defined structure of the TiS2 nanosheets (3.86 nm with 1T phase) was constructed. These as-prepared catalysts were applied to the electrochemical reductive amination (ERA) of three biomass-derived aldehydes, i.e. furfural (FF), 5-methylfurfural (MF) and 5-hydroxymethylfurfural (HMF), and exhibited superior performance whereby over 95% conversion of each furanic aldehyde and nearly perfect selectivity of ONCs were achieved. TiS2 nanosheets, in particular, exhibited a marked ∼2-fold increase in conversion (∼49%) compared with the monometallic Ti electrode. Besides, the reaction kinetics and rational pathway were also studied. In addition, these exfoliated TiS2 nanosheets maintained high durability over 6 h, providing a promising and versatile route for the sustainable upgrading of biomass-derived sources. [ABSTRACT FROM AUTHOR]

Urban road facilities are typically shared by heterogeneous traffic, such as cars, buses, and bicycles. This paper investigates the car and bicycle traffic at the network level by extending the 3D-MFD model to the car-bicycle network. Statistical 3D-MFDs for the car-bicycle traffic are proposed and empirically estimated for three selected networks in Shanghai. Simulation-based 3D-MFDs are used to determine the network bicycle capacity and validate statistical 3D-MFDs. Estimation results quantify negative marginal effects of car and bicycle density on network car speeds, and confirm the negative effect of bi-modal speed difference on bicycle traffic. When looking into the predicted results of 3D-MFDs, the maximum bi-modal production changes with the accumulation of bicycles, indicating car-bicycle interactions within the network. Few empirical observations are in the optimum operation regime, which indicate the potential of improving the multi-modal networks. Findings of this study can benefit network designs and traffic operations, such as adjusting separation types, implementing stricter bicycle management, and extending bicycle priority in certain areas. [ABSTRACT FROM AUTHOR]

Prediction of rock fracturing capacity demands particular requirements for the exploitation of mineral resources, especially for the parameter design of conical pick performance for hard rock fragmentation, which must take into account differences in rock mechanical properties. Among these parameters, the peak cutting force (PCF) is important in designing, selecting, and optimizing the cutting head of mining equipment and a cutability index of rocks. Taking high lithological tolerance as demand traction, this study proposes a theoretical model for estimating the peak cutting force of conical picks based on the improved projection profile method for which the influence of alloy head, pick body structure, and installation parameters are taken into consideration. Besides, experimental results corresponding to different numbers of rock samples are used to verify the accuracy and stability of the theoretical model. Meanwhile, the comparison of performance in cutting force estimation between this model and four other existing theoretical models is conducted. The results found that the new method has the highest correlation coefficient with the experimental results and the lowest root mean square error comparing with other models, i.e., the estimation performance of this method has high lithological tolerance when the rock type increases and the lithology changes. Consequently, the proposed peak cutting force estimation of improved projection profile method will provide a more valid and accurate prediction for rock fracturing capacity with large differences in rock mechanical properties. [ABSTRACT FROM AUTHOR]

Rolling bearings are an important part of rotating machinery, and are of great significance for fault diagnosis and life monitoring of rolling bearings. Analyzing fault signals, extracting effective degradation information and establishing corresponding models are the premise of residual life prediction of rolling bearings. In this paper, first, the time-domain features were extracted to form the eigenvector of the vibration signal, and then the index representing the bearing degradation was found. It was found that the time-domain index could effectively describe the degradation information of the bearing, and the multi-dimensional time-domain characteristic information could effectively describe the attenuation trend of the vibration signal of the rolling bearing. On this basis, appropriate feature vectors were selected to describe the degradation characteristics of bearings. Aiming at the problems of large amounts of data, large amounts of information redundancy and unclear performance index of multi-dimensional feature vectors, the dimensionality of multi-dimensional feature vectors was reduced with principal component analysis, thus, simplifying the multi-dimensional feature vectors and reducing the information redundancy. Finally, in view of the support vector machine (SVM)'s needs to determine kernel function parameters and penalty factors, the squirrel optimization algorithm (SOA) was used to adaptively select parameters and establish the state-life evaluation model of rolling bearings. In addition, mean absolute error (MAE) and root mean squared error (RMSE) were used to comprehensively evaluate SOA. The results showed that the SOA reduced the errors by 5.1% and 13.6%, respectively, compared with a genetic algorithm (GA). Compared with particle swarm optimization (PSO), the error of SOA was reduced by 7.6% and 15.9%, respectively. It showed that SOA-SVM effectively improved the adaptability and regression performance of SVM, thus, significantly improving the prediction accuracy. [ABSTRACT FROM AUTHOR]