With the rapid development of artificial intelligence and industrial Internet of Things (IIoT) technologies, intelligent predictive maintenance (IPdM) has received considerable attention from researchers and practitioners. To efficiently predict impending failures and mitigate unexpected downtime, while satisfying the instant maintenance demands of industrial facilities is very important for improving the production efficiency. In this article, a self-attention based “Perception and Prediction” framework, called DeepHealth, is proposed for the instant IPdM. Specifically, the framework is composed of two submodels (i.e., DH-1 and DH-2), which are respectively utilized to perform the health perception and sequence prediction. By operating the framework, the proposed models can predict the health conditions via predicting the future signal samples, thereby completing the instant IPdM. Considering the potential temporal correlation in time series, we deploy an enhanced attention mechanism to capture global dependencies from the vibration signals, and leverage the long- and short-term sequence prediction of sensor signals to support instant maintenance decision-making. On this basis, we conduct a destructive experiment based on the IIoT-enabled rotating machinery and construct a balanced industrial dataset for model evaluations. Extensive experiment results show that the proposed solution achieves good prediction accuracy for instant IPdM on the automatic washing equipment and Case Western Reserve University datasets.

In this paper, a Pedestrian Risky Level Prediction (PRLP) model based on the data collected by a vehicle-mounted sensing system is proposed. The prediction of pedestrian risky level is realized using a data-driven method, which contains two steps, namely pedestrian trajectory prediction and risky level classification. For trajectory prediction, an LSTM network is built and trained to predict the trajectory of pedestrian in the onboard camera view. For risky level classification, to avoid manually selecting thresholds for different risky levels, a hybrid method integrating K-Means Clustering (KMC), Kernel Principal Components Analysis (KPCA) and Kernel Support Vector Machine (SVM) is applied. The pedestrian risky level is divided into four classes by KPCA-KMC, namely low risk, medium risk, high risk and super-high risk. Experimental results show the capability of PRLP model to predict the risky level of pedestrian, which could help the intelligent vehicle to assess the risk of collision with pedestrians, and provide safety warnings to both vehicle and pedestrian.

Because of the high accuracy and low cost, learning-based methods have been widely used to model driver behaviors in various scenarios. However, the performance of learning-based methods depend heavily on the quantity and coverage of the driving data. When the new driver with insufficient data is considered, the accuracy of these methods cannot be guaranteed any more. To solve this problem, the balanced distribution adaptation (BDA) is used to build the new driver's decision making model in the lane change (LC) scenario. Meanwhile, a transfer learning (TL) based regression model, modified BDA (MBDA) is proposed to predict the driver's steering behavior during the LC maneuver. Cross validation (CV) based model selection (MS) method is developed to obtain the optimal parameters in model training process. A series of experiments are carried out based on the simulated and naturalistic driving data to verify the TL based classification and regression models. The experimental results indicate that the BDA and MBDA have an outstanding ability in knowledge transfer. Compared with support vector machine (SVM) and Gaussian mixture regression (GMR), the proposed methods show a better performance in the decision making of lane keep/change and the prediction of the driver's steering operation.

Industrial wireless sensor networks (IWSNs) have mainly been used to monitor applications, but recently an interest in control and safety applications has emerged. Functional safety and communication in open transmission systems have been laid down in the IEC 61784-3-3 standard. The standard is based on a cyclic polling mechanism, which consumes a considerable amount of bandwidth; since existing IWSNs are very resource-constrained, this becomes a major challenge. To overcome this problem, this paper proposes a novel framework that uses an event-triggered failsafe mechanism based on synchronous wired polling and wireless time-slotted time division multiple access. We analytically derive the minimum and maximum bound for the most important metric for safety-critical applications, safety function response time (SFRT). A new metric, normal state interrupt time (NSIT), is proposed in this paper. Furthermore, we also implement the proposed framework by using the WirelessHART standard. The results are compared to the classical time-triggered approach used in the IEC 61784-3-3 standard. The obtained results show that the proposed framework can reduce the bandwidth usage by 90% and support safety-critical applications that require a SFRT less or equal to 150 ms.

To provide sufficient 3D-depth fidelity, integral imaging (II) requires an increase in spatial resolution of several orders of magnitude from today's 2D images. We have recently proposed a pre-processing and compression scheme for still II-frames based on forming a pseudo video sequence (PVS) from sub images (SI), which is later coded using the H.264/MPEG-4 AVC video coding standard. The scheme has shown good performance on a set of reference images. In this paper we first investigate and present how five different ways to select the SIs when forming the PVS affect the schemes compression efficiency. We also study how the II-frame structure relates to the performance of a PVS coding scheme. Finally we examine the nature of the coding artifacts which are specific to the evaluated PVS-schemes. We can conclude that for all except the most complex reference image, all evaluated SI selection orders significantly outperforms JPEG 2000 where compression ratios of up to 342:1, while still keeping PSNR > 30 dB, is achieved. We can also confirm that when selecting PVS-scheme, the scheme which results in a higher PVS-picture resolution should be preferred to maximize compression efficiency. Our study of the coded II-frames also indicates that the SI-based PVS, contrary to other PVS schemes, tends to distribute its coding artifacts more homogenously over all 3D-scene depths.

The next evolutionary step in enhancing video communication fidelity is taken by adding scene depth. 3D video using integral imaging (II) is widely considered as the technique able to take this step. However, an increase in spatial resolution of several orders of magnitude from todays 2D video is required to provide a sufficient depth fidelity, which includes motion parallax. In this paper we propose a pre-processing and compression scheme that aims to enhance the compression efficiency of integral images. We first transform a still integral image into a pseudo video sequence consisting of sub-images, which is then compressed using an H.264 video encoder. The improvement in compression efficiency of using this scheme is evaluated and presented. An average PSNR increase of 5.7 dB or more, compared to JPEG 2000, is observed on a set of reference images.

We present an adaptive technique that combines forward error correction (FEC) with channel state estimation (CSE) and automatic repeat request (ARQ) for packet loss recovery in wireless indoor multicast systems. The novel aspect of the technique is its ability to achieve significant packet throughput with high data reliability, to avoid feedback acknowledgement (ACK) implosion, as well as to reduce and flexibly to limit delay for real-time applications. We suggest a set of criteria to measure the multicast performance and simulate a simple two-state indoor channel model. The simulation results show that the proposed technique significantly improves the communication quality and channel efficiency, to compare with traditional FEC and ARQ techniques.