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Optimal sensor placement is an important component of a reliability structural health monitoring system for a large-scale complex structure. However, the current research mainly focuses on optimizing sensor placement problem for structures without any initial sensor layout. In some cases, the experienced engineers will first determine the key position of whole structure must place sensors, that is, initial sensor layout. Moreover, current genetic algorithm or partheno-genetic algorithm will change the position of the initial sensor locations in the iterative process, so it is unadaptable for optimal sensor placement problem based on initial sensor layout. In this article, an optimal sensor placement method based on initial sensor layout using improved partheno-genetic algorithm is proposed. First, some improved genetic operations of partheno-genetic algorithm for sensor placement optimization with initial sensor layout are presented, such as segmented swap, reverse and insert operator to avoid the change of initial sensor locations. Then, the objective function for optimal sensor placement problem is presented based on modal assurance criterion, modal energy criterion, and sensor placement cost. At last, the effectiveness and reliability of the proposed method are validated by a numerical example of a quayside container crane. Furthermore, the sensor placement result with the proposed method is better than that with effective independence method without initial sensor layout and the traditional partheno-genetic algorithm.

Accurate remaining useful life (RUL) prediction has a great significance to improve the reliability and safety for key equipment. However, it often occur imperfect or even no prior degradation information in practical application for the existing RUL prediction methods, which could produce prediction error. To solve this issue, this paper proposes a two-step RUL prediction method based on Wiener processes with reasonably fusing the failure time data and field degradation data. First, we obtain some interesting natures of parameters estimation based on the basic linear Wiener process. These natures explain the relationship between the parameters estimation results and the feature of degradation data, i.e. item sample numbers, detection time and detect frequency, and give the basis regarding how to reasonably fuse the failure time data and field degradation data. Second, under the Bayesian framework, we further propose a two-step method by fusing the failure time data and field degradation data with considering the random effects based on the proposed natures of parameters estimation. In this method, we propose an EM algorithm to estimate the mean and variance drift parameter of Wiener processes by the failure time data. Next, we generalize this two-step RUL prediction method to the nonlinear Wiener process. Last, we use two case studies to demonstrate the usefulness and superiority of the proposed method.

The nonlinear Wiener process has been widely used as a model for the degradation process. This note concerns parameters estimation of nonlinear Wiener processes with measurement errors (WPME) by the maximum likelihood estimation method. Firstly, we prove a rule that the estimated results based on the sample likelihood function developed through observations at each point are equal to the results from the first differences of the observations. This rule indicates that for reducing computation complexity the first differences of the observations may develop the sample likelihood function. Then we present a simple method to calculate the determinant and the inverse matrix of the covariance matrix of the WPME. This simple method could avoid the overflow error when calculating the determinant of the covariance matrix and the case that the inverse matrix is close to be singular, which could result in wrong estimation results. Secondly, we highlight the unit-specific assumption, which has a significant impact on parameters estimation but has been neglected in many papers. Then, we propose a modified expectation maximization algorithm for parameters estimation with random effects. Finally, to demonstrate the application and superiority of the proposed method, we provide a numerical example and a case study with comparison to several representative methods in the literature.

State-of-Health (SOH) is an intuitive reflection to the capacity during the degradation of lithium-ion batteries. Accurate SOH estimation can not only grasp the battery performance but also achieve a better balance between the safety and economic benefits for lithium-ion battery application system. Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during long rest time. This paper mainly studies the impact of relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel SOH estimation method based on the Wiener process. First, the life cycle of a lithium-ion battery is divided into three parts, i.e., the degradation process that eliminates the relaxation effect, the capacity regeneration process during the rest time, and the degradation process of the regenerated capacity. Next, the degradation model after eliminating the relaxation effect is established based on linear Wiener process, the capacity regenerated model is developed by the normal distribution, and the degradation model of regenerated capacity is established based on a nonlinear Wiener process. Then, for the degradation model based on nonlinear Wiener process, a two-step maximum likelihood estimation (MLE) method for prior parameters is proposed, and the random variables representing the personality features are updated online under the Bayesian framework. For the capacity regenerated model, a parameter estimation method based on MLE is proposed. In addition, a one-step and a multi-step SOH prediction method based on piecewise modeling are developed. Finally, the experiments are carried out based on the degradation data of lithium-ion batteries published by the NASA, and the results show that the method proposed in this paper can effectively improve the accuracy of the SOH estimation.

Safety assessments are significant because they can identify the potential risks in complex systems and achieve condition-based maintenance with lower expenses. The safety assessment of a complex system requires an efficient model that can handle numerous correlated features with multiple information sources with uncertainties and reflect complicated nonlinear relationships. Therefore, an optimal safety assessment model for complex systems considering correlation and redundancy is proposed in this paper. First, a two-stage feature selection method is proposed in which the sensitivity factor is adopted to select the relevant features and the maximal information coefficient (MIC) is used to analyze the redundancy between features. Second, weak correlation between features is eliminated by discounting a feature's weight based on the correlation factor. Third, an optimized belief rule base (BRB) is utilized to model the complicated nonlinear relationships of complex systems. Moreover, the referential values for attributes in the BRB are expressed in interval form, which is more consistent with reality. Verification experiments are conducted on a comprehensive gearbox platform and for a real-world case. Compared to the other assessment approaches, the new model has superior attributes, such as higher accuracy and stronger robustness.

Defocus of the imaging system, relative motion between the equipment and the object or inherent defects of the equipment will lead to the degradation of image quality. Usually, image restoration is required before image processing, and the results of restoration technology affect the effect of image processing. In order to study the effect of image restoration technology, the image restoration technology was systematically sorted out. According to the different restoration models, the image restoration technology is divided into the method based on regularization and the method based on Kalman filter, and the two methods are summarized and explained respectively. By analyzing the restoration models established by the two methods and their solving process, it is found that the method based on regularization has advantages in retaining image information, and the method based on Kalman filter has advantages in terms of speed. The development trend of image restoration technology is analyzed from the summary of the two restoration methods, and suggestions and thoughts are put forward for the development of image restoration technology. In the future, image restoration technology should make use of deep learning algorithm, combined with the actual environment and industry characteristics, to achieve intelligent, practical and domain.

Reversible data hiding in encrypted image (RDH-EI) is a hot topic of data hiding in recent years. Most RDH-EI algorithms do not reach desirable embedding rate and their computational costs are not suitable for real-time applications. Aiming at these problems, we propose an efficient RDH-EI algorithm with shifting block histogram of pixel differences in homomorphic encrypted domain. A key step of our RDH-EI algorithm is the block-based encryption scheme with additive homomorphism, which can preserve spatial correlation of plaintext image in homomorphic encrypted domain. In addition, our proposed technique of shifting block histogram can achieve efficient data embedding with high payload and correctly recover image. Extensive experiments are conducted to validate performance of our RDH-EI algorithm. Comparison results illustrate that our RDH-EI algorithm outperforms some state-of-the-art algorithms in terms of embedding rate, visual quality and computational time.

Time-varying temperature condition has a significant impact on discharge capacity and aging law of lithium-ion battery. Consequently, a novel remaining useful life (RUL) prediction method for lithium-ion battery under time-varying temperature condition is developed in this paper. Firstly, a stochastic degradation rate model based on Arrhenius temperature model is proposed, and an interesting battery capacity conversion path from random temperature condition to reference temperature condition is established. Secondly, the aging model of lithium-ion battery under time-varying temperature condition is developed based on Wiener process, and a two-step unbiased estimation method based on maximum likelihood estimation (MLE) combined with genetic algorithm (GA) is proposed. Next, the random parameter is online updated under Bayesian framework. Then the probability density function (PDF) of the RUL for lithium-ion battery under time-varying temperature condition is derived. Finally, a case study is implemented to verify the effectiveness, and the results show that the proposed prediction method has higher accuracy and smaller uncertainty.

Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value.

Remaining useful life (RUL) estimation based on condition monitoring data is central to condition based maintenance (CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncated normal distribution to model random failure threshold (RFT), an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter. Then, the Bayesian method was used to update the prior estimation of failure threshold. To solve the uncertainty of the censored in situ data of failure threshold, the expectation maximization (EM) algorithm is used to calculate the posteriori estimation of failure threshold. Numerical examples show that considering the randomness of the failure threshold and updating the prior information of RFT could improve the accuracy of real time RUL estimation.

Deteriorating systems, which are subject to both continuous smooth degradation and additional abrupt damages due to a shock process, can be often encountered in engineering. Modeling the degradation evolution and predicting the lifetime of this kind of systems are both interesting and challenging in practice. In this paper, we model the degradation trajectory of the deteriorating system by a random coefficient regression (RCR) model with positive jumps, where the RCR part is used to model the continuous smooth degradation of the system and the jump part is used to characterize the abrupt damages due to random shocks. Based on a specified threshold level, the probability density function (PDF) and cumulative distribution function (CDF) of the lifetime can be derived analytically. The unknown parameters associated with the derived lifetime distributions can be estimated via a well-designed parameter estimation procedure on the basis of the available degradation recordings of the deteriorating systems. An illustrative example is finally provided to demonstrate the implementation and superiority of the newly proposed lifetime prediction method. The experimental results reveal that our proposed lifetime prediction method with the dedicated parameter estimation strategy can get more accurate lifetime predictions than the rival model in literature.

Real time remaining useful life (RUL) prediction based on condition monitoring is an essential part in condition based maintenance (CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item’s individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.

In allusion to the problem that bus break fault can’t be detected when CAN bus is not in the process of data changing, a new method that through adding high frequency signal generator and corresponding detection unit on the bus network has been brought forward, and the CAN bus break fault detection method has been realized. In this paper, detection model of CAN bus break fault is constructed, the 5 MHz sinusoidal signal generator and circuit design of detection unit are also provided. Finally, the rationality of the new method is verified by a 4 nodes network. The research conclusions indicate that bus break fault detection can be detected in real-time and completely by this way. Besides, the provided method is not occupy the network band width and can solve the existing detection institution problem effectively, the apply prospect is very fine in the occasion whose reliability requirement is relatively high.

Remaining useful life (RUL) prediction is central to the prognostics and health management (PHM) of lithium-ion batteries. This paper proposes a novel RUL prediction method for lithium-ion batteries based on the Wiener process with measurement error (WPME). First, we use the truncated normal distribution (TND) based modeling approach for the estimated degradation state and obtain an exact and closed-form RUL distribution by simultaneously considering the measurement uncertainty and the distribution of the estimated drift parameter. Then, the traditional maximum likelihood estimation (MLE) method for population based parameters estimation is remedied to improve the estimation efficiency. Additionally, we analyze the relationship between the classic MLE method and the combination of the Bayesian updating algorithm and the expectation maximization algorithm for the real time RUL prediction. Interestingly, it is found that the result of the combination algorithm is equal to the classic MLE method. Inspired by this observation, a heuristic algorithm for the real time parameters updating is presented. Finally, numerical examples and a case study of lithium-ion batteries are provided to substantiate the superiority of the proposed RUL prediction method.

Preceding vehicle detection and tracking at nighttime are challenging problems due to the disturbance of other extraneous illuminant sources coexisting with the vehicle lights. To improve the detection accuracy and robustness of vehicle detection, a novel method for vehicle detection and tracking at nighttime is proposed in this paper. The characteristics of taillights in the gray level are applied to determine the lower boundary of the threshold for taillights segmentation, and the optimal threshold for taillight segmentation is calculated using the OTSU algorithm between the lower boundary and the highest grayscale of the region of interest. The candidate taillight pairs are extracted based on the similarity between left and right taillights, and the non-vehicle taillight pairs are removed based on the relevance analysis of vehicle location between frames. To reduce the false negative rate of vehicle detection, a vehicle tracking method based on taillights estimation is applied. The taillight spot candidate is sought in the region predicted by Kalman filtering, and the disturbed taillight is estimated based on the symmetry and location of the other taillight of the same vehicle. Vehicle tracking is completed after estimating its location according to the two taillight spots. The results of experiments on a vehicle platform indicate that the proposed method could detect vehicles quickly, correctly and robustly in the actual traffic environments with illumination variation.

Accelerated degradation tests (ADT) modeling is an important issue in lifetime assessment to the products with high reliability and long lifetime. Among the literature about the accelerated nonlinear degradation process modeling, the current methods did not consider the product-to-product variation of the products with the same type. Therefore, this paper proposes an accelerated degradation process modeling method with random effects for the nonlinear Wiener process. Firstly, we derive the lifetime distribution of the nonlinear Wiener process with random effects. Secondly, the nonlinear Wiener process is used to model the degradation process of a single stress, and the drift coefficient is considered as a random variable to describe the product-to-product variation. Using the random acceleration model, the random effects are incorporated into the constant stress ADT models and the step stress ADT models. Then, a two-step maximum likelihood estimation (MLE) method is presented to estimate the unknown parameters in the degradation models. Finally, a simulation study and a case study are provided to demonstrate the application and superiority of the proposed model.

To enhance the reliability of the CAN bus, the relevant research in the CAN design and the Fault Diagnosis of the CAN-bus is carried out. The advantages of the analytical redundancy fault-tolerant control concepts and the hardware redundancy fault-tolerant control concept are effectively integrated and the control area network based on fault-tolerant redundancy technology is designed according to these ideas; an experiment setup is built to perform the Hard Fault detection of three CAN nodes. The experiment results verified that the resistance detection method can detect and locate the fault point of the CAN-bus permanent failure.