•Propose a novel deep convolutional neural network-based method for remaining useful life predictions.•No prior expertise on prognostics and signal processing is required, that facilitates the ...application of the proposed method.•Effects of the key factors on the prognostic performance are widely investigated and the model parameters are optimized.•Experiments on a popular aero-engine degradation dataset (C-MAPSS) and comparisons with the related state-of-the-art results validate the effectiveness and superiority of the proposed method.
Traditionally, system prognostics and health management (PHM) depends on sufficient prior knowledge of critical components degradation process in order to predict the remaining useful life (RUL). However, the accurate physical or expert models are not available in most cases. This paper proposes a new data-driven approach for prognostics using deep convolution neural networks (DCNN). Time window approach is employed for sample preparation in order for better feature extraction by DCNN. Raw collected data with normalization are directly used as inputs to the proposed network, and no prior expertise on prognostics and signal processing is required, that facilitates the application of the proposed method. In order to show the effectiveness of the proposed approach, experiments on the popular C-MAPSS dataset for aero-engine unit prognostics are carried out. High prognostic accuracy on the RUL estimation is achieved. The superiority of the proposed method is demonstrated by comparisons with other popular approaches and the state-of-the-art results on the same dataset. The results of this study suggest that the proposed data-driven prognostic method offers a new and promising approach.
Intelligent machinery fault diagnosis system has been receiving increasing attention recently due to the potential large benefits of maintenance cost reduction, enhanced operation safety and ...reliability. This paper proposes a novel deep learning method for rotating machinery fault diagnosis. Since accurately labeled data are usually difficult to obtain in real industries, data augmentation techniques are proposed to artificially create additional valid samples for model training, and the proposed method manages to achieve high diagnosis accuracy with small original training dataset. Two augmentation methods are investigated including sample-based and dataset-based methods, and five augmentation techniques are considered in general, i.e. additional Gaussian noise, masking noise, signal translation, amplitude shifting and time stretching. The effectiveness of the proposed method is validated by carrying out experiments on two popular rolling bearing datasets. Fairly high diagnosis accuracy up to 99.9% can be obtained using limited training data. By comparing with the latest advanced researches on the same datasets, the superiority of the proposed method is demonstrated. Furthermore, the diagnostic performance of the deep neural network is extensively evaluated with respect to data augmentation strength, network depth and so forth. The results of this study suggest that the proposed intelligent fault diagnosis method offers a new and promising approach.
This paper proposes a multiobjective optimization method for signal control design at intersections in urban traffic network. The cell transmission model is employed for macroscopic simulation of the ...traffic. Additional rules are introduced to model different route choices from origins to destinations. Vehicle turning, merging, and diverging behaviors at intersections are considered. A multiobjective optimization problem (MOP) is formulated considering four measures in network traffic performance, i.e., maximizing system throughputs, minimizing traveling delays, enhancing traffic safety, and avoiding spillovers. The design parameters for an intersection include turning signal type, cycle time, signal offset, and green time in each phase. The resulting high-dimensional MOP is solved with the genetic algorithm (GA). An algorithm is proposed to assist the user to select and implement the optimal designs from the Pareto optimal solution set. A case study in a grid network of nine intersections is carried out to test the optimization algorithm. It is observed that the proposed method is able to achieve the optimal network performance with different traffic demands. The convergence and coefficient selection of GA are discussed. The guidelines for network signal design and operation from the current studies are presented.
•A novel domain adaptation method is proposed to solve domain shift problem.•Multi-Layer and multi-kernel MMD between source and target domains is minimized.•Cross-domain fault diagnosis performance ...on rolling bearings is significantly improved.•The proposed method is promising for applications in different industrial scenarios.
In the past years, data-driven approaches such as deep learning have been widely applied on machinery signal processing to develop intelligent fault diagnosis systems. In real-world applications, domain shift problem usually occurs where the distribution of the labeled training data, denoted as source domain, is different from that of the unlabeled testing data, known as target domain. That results in serious diagnosis performance degradation. This paper proposes a novel domain adaptation method for rolling bearing fault diagnosis based on deep learning techniques. A deep convolutional neural network is used as the main architecture. The multi-kernel maximum mean discrepancies (MMD) between the two domains in multiple layers are minimized to adapt the learned representations from supervised learning in the source domain to be applied in the target domain. The domain-invariant features can be efficiently extracted in this way, and the cross-domain testing performance can be significantly improved. Experiments on two rolling bearing datasets are carried out to validate the effectiveness of the domain adaptation approach. Comparisons with other approaches and related works demonstrate the superiority of the proposed method. The experimental results of this study suggest the proposed domain adaptation method offers a new and promising tool for intelligent fault diagnosis.
Real-time traffic speed estimation is an essential component of intelligent transportation system (ITS) technologies. It is the foundation of modern transportation control and management ...applications. However, the existing traffic speed acquisition systems can only provide real-time speed measurements of a small number of roads with stationary speed sensors and crowdsourcing vehicles. How to utilize this information to provide traffic speed maps for transportation networks is becoming a key problem in ITSs. In this paper, we present a novel deep-learning model called graph convolutional generative autoencoder to fully address the real-time traffic speed estimation problem. The proposed model incorporates the recent development in deep-learning techniques to extract the spatial correlation of the transportation network from the input incomplete historical data. To evaluate the proposed speed estimation technique, we conduct comprehensive case studies on a real-world transportation network and vehicular traces. The simulation results demonstrate that the proposed technique can notably outperform existing traffic speed estimation and deep-learning techniques. In addition, the impact of dataset properties and control parameters is investigated.
•A new force amplifying mechanism to fully utilize piezoelectrical conversion potential.•A piezoelectric energy harvesting device with highest density so far.•An innovative design for implementing ...clamped boundary condition.•Laboratory tests on prototype validate the model and mechanical design.•Dynamic traffic load on the device and its effect on energy harvesting.
This paper introduces an innovative piezoelectric energy harvesting device with a high density of the energy harvested from highway traffic. The piezoelectric energy harvesting device has a compression-to-compression force amplification mechanism provided by clamped-clamped nonlinear elastic beams. The amplification mechanism enables the device to fully explore the power conversion potential of the piezoelectric material and can deliver the harvested electricity far more than that generated by the same piezoelectric material under direct compressive loading without amplification. For example, when the amplification factor is equal to 10, the generated electricity is nearly 100 times bigger. A multi-objective optimal design problem of the piezoelectric energy harvesting device is formulated. A set of multi-objective optimal designs has been found numerically in the parameter space. The paper presents the analysis of the nonlinear beams and a finite element model of multi-layer piezoelectric ceramic stacks. Impulsive time dynamic loadings from the passing vehicles are considered. The dynamic response of the piezoelectric energy harvesting device to the impulsive traffic loadings is investigated. The laboratory test results are presented to validate the mathematical model and mechanical design of the device. In a quasi-static load cycle of 1333N, a preloaded piezoelectric energy harvesting device prototype (142×42×84mm3) is able to generate a voltage of 128V and a potential electric energy of 120mJ while the sinking displacement is 2.54mm. Numerical results of the dynamic response show that the piezoelectric energy harvesting device performs well over a wide range of vehicle speed from 8.05 to 128.75 km/h. High speed seems beneficial to energy harvesting.
California is the world's biggest producer and exporter of almonds. Currently, the sweeping of almonds during the harvest creates a significant amount of dust, causing air pollution in the ...neighboring urban areas. A low-dust sweeping system was designed to reduce the dust during the sweeping of almonds in the orchard. The system includes a feedback control system to control the sweeper brushes' height and their angular velocity by adjusting the forward velocity of the harvester and the brushes' rotational speeds to avoid any extra overlapping sweeping, which increases dust generation. The governing kinematic equations for sweepers' angular velocity and vehicle forward speed were derived. The feedback controllers for synchronizing these speeds were designed to optimize brush/dust contact to minimize dust generation. The sweepers' height controller was also designed to stabilize the gap between the brushes and the orchard floor and track the road trajectory. Controllers were simulated and tuned for a fast response for agricultural applications with less than a second response delay. Results showed that the designed system has acceptable performance and generates low amounts of dust within the acceptable range of California ambient air quality standards.
Systems exposed to random excitations may fail long before stationarity is achieved, which necessitates consideration of the transient responses of the system. On the other hand, the transient ...response prediction of non-smooth systems may face more obstacles than that of smooth systems. One of the foremost challenges lies in the handling of vector fields with singularities in non-smooth systems. In this work, the radial basis function neural networks (RBFNN) is utilized for the first time to analyze the transient response of randomly excited vibro-impact systems (VIS), a class of typical non-smooth systems. The solution of the system response is expressed in terms of a serious of Gaussian activation functions (GAFs) with time-dependent weights. These time-dependent weights are determined by minimizing a loss function, which involves the residual of the differential equations and constraint conditions. To avoid the singularity of the initial condition being a Dirac delta function, a strategy of short-time Gaussian approximation is presented to obtain the initial weights. Two typical VISs exposed to stochastic excitations are presented to verify the suggested scheme. Appropriate comparisons to the data obtained by digital simulation show that the method yields reliable results even for strongly nonlinear systems. With the merits of high computational accuracy and satisfactory efficiency, this approach is expected to be an effective method for solution of random vibration problems of high-dimensional complex non-smooth systems.
•Transient responses of the randomly excited vibro-impact system was estimated using RBFNN.•Short-time Gaussian approximation is employed to avoid the singularity of initial values.•The strong nonlinear characteristic of vibro-impact system is completely captured.•Comprehensive numerical comparison shows the validity of proposed scheme.
Drivers often change lanes on the road to maintain desired speed and to avoid slow vehicles, pedestrians, obstacles and lane closure. Understanding the effect of lane-changing on the traffic is an ...important topic in designing optimal traffic control systems. This paper presents a comprehensive study of this topic. We review the theory of microscopic dynamic car-following models and the lane-changing models, propose additional lane-changing rules to deal with moving bottleneck and lane reduction, and investigate the effects of lane-changing on the traffic efficiency, traffic safety and fuel consumption as a function of different variables including the distance of the emergency sign ahead of the lane closure, speed limit, traffic density, etc. Extensive simulations of the traffic system have been carried out in different scenarios. A number of important findings of the effect of various factors on the traffic are reported. These findings provide guidance on the traffic management and are important to the designers and engineers of modern highway or inner city roads to achieve high traffic efficiency and safety with minimum environmental impact.
•Presented a combined model of vehicle lane-changing with road bottleneck.•Studied effects of vehicle lane-changing on traffic, safety and energy economy.•Discovered situations when vehicle lane-changing can be beneficial.•Studied negative consequences of lane-changing.
Objective
To analyze the early complications and causes of oblique lateral interbody fusion, and put forward preventive measures.
Methods
There were 235 patients (79 males and 156 females) analyzed ...in our study from October 2014 to May 2017. The average age was 61.9 ± 0.21 years (from 32 to 83 years). Ninety‐one cases were treated with oblique lateral interbody fusion (OLIF) alone (OLIF alone group) and 144 with OLIF combined with posterior pedicle screw fixation through the intermuscular space approach (OLIF combined group). In addition, 137/144 cases in the combined group were primarily treated by posterior pedicle screw fixation, while the treatments were postponed in 7 cases. There were 190 cases of single fusion segments, 11 of 2 segments, 21 of 3 segments, and 13 of 4 segments. Intraoperative and postoperative complications were observed.
Results
Average follow‐up time was 15.6 ± 7.5 months (ranged from 6 to 36 months). Five cases were lost to follow‐up (2 cases from the OLIF alone group and 3 cases from the OLIF combined group). There were 7 cases of vascular injury, 22 cases of endplate damage, 2 cases of vertebral body fracture, 11 cases of nerve injury, 18 cases of cage sedimentation or cage transverse shifting, 3 cases of iliac crest pain, 1 case of right psoas major hematoma, 2 cases of incomplete ileus, 1 case of acute heart failure, 1 case of cerebral infarction, 3 case of left lower abdominal pain, 9 cases of transient psoas weakness, 3 cases of transient quadriceps weakness, and 8 cases of reoperation. The complication incidence was 32.34%. Thirty‐three cases occurred in the OLIF alone group, with a rate of 36.26%, and 43 cases in the group of OLIF combined posterior pedicle screw fixation, with a rate of 29.86%. Fifty‐seven cases occurred in single‐segment fusion, with a rate of 30.0% (57/190), 4 cases occurred in two‐segment fusion, with a rate of 36.36% (4/11), 9 cases occurred in three‐segment fusion, with a rate of 42.86% (9/21), and 6 cases occurred in four‐segment fusion, with a rate of 46.15% (6/13).
Conclusion
In summary, OLIF is a relatively safe and very effective technique for minimally invasive lumbar fusion. Nonetheless, it should be noted that OLIF carries the risk of complications, especially in the early stage of development.
PDF
