Rotating machinery plays an essential role in various industrial processes such as manufacturing, power generation, and transportation. These machines, which include turbines, pumps, motors, compressors, and many others, are the heartbeats of numerous industries. The seamless operation of these machines is critical for the efficiency and productivity of these sectors. However, over time, these machines degrade and can suffer faults. One of the most critical components are bearings, which can suffer different types of faults. This paper presents a novel approach for bearing fault classification and diagnosis under limited data. A Monotonic Smoothed Stacked AutoEncoder (MS2AE) is used to infer a smoothed monotonic health index from raw bearing acceleration data. The MS2AE is trained using only healthy data, so this approach can also be used with recently comisioned equipment that has not failed yet. Then, using the evolution of the health index, a first faulty point is computed, so two stages are identified in the lifespan of the rotating machinery: healthy and faulty. Correlation matrices are computed to show the relationship of the health index with time-domain and frequency-domain features in order to provide explainability and validate the health index construction process. When the health index is classified as faulty, Dynamic Time Warping is applied between healthy samples and faulty samples to extract differences. Finally, based on a 1/3-binary tree 3 level kurtogram, these differences are filtered using a bandpass filter and converted to the frequency domain, where characteristic harmonics are used to identify the type of bearing fault. The explainability provided in the health index construction process makes the system useful in certain industries where black-box AI models cannot be trusted due to strict regulations. The classification and diagnosis system achieves robustness in fault classification under different working conditions by utilizing multiple bearing fault datsets. Its ability to be trained using only healthy data and the interpretability offered, makes it suitable for recently installed rotating machinery in real industrial facilities, without requiring qualified staff.

Predicting the remaining useful lifetime (RUL) of bearings in electric motors is crucial to reduce repair costs in industrial maintenance. With the technological advances of Industry 4.0, physical models for prognostics and RUL prediction have been replaced by data-driven models that require no expert feature extraction. Instead, the model itself learns which features are important. However, these models are normally trained and tested on the same dataset, i.e. under the same operating conditions. This limits the application of a model to other operating conditions unless the model is fine-tuned with data corresponding to those conditions. This paper proposes a novel robust health prognostics technique that detects inner-race bearing failures and predicts the RUL of electric motor bearings under various motor conditions without model retraining or fine-tuning. The model combines time and frequency-domain vibration signal analyses to extract features, a stacked variational denoising autoencoder (SVDAE) to fuse these features and build a Health Indicator and a bidirectional long short-term memory (BiLSTM) neural network to predict the remaining useful lifetime of the bearings. The proposed model is trained with a dataset, validated with another dataset and finally tested with seven additional datasets corresponding to vibrations gathered from different motors and operating conditions. The results are more robust and accurate than those of the literature, the robustness of the prediction with different motor and operating conditions is proven, and there is no need to retrain or fine-tune the model, making the proposed model suitable for recently installed equipment.

Abstract Condition monitoring of industrial equipment has become a critical aspect in Industry 4.0. This paper shows the design, implementation and testing of a low-cost Industrial Internet of Things (IIoT) system designed to monitor electric motors in real-time. This system can be used to detect operating anomalies and paves the way for building predictive maintenance models. The system is built using low-cost hardware components (wireless multi-sensor modules and single-board computers as gateways), open-source software and open cloud services, where all the relevant information is stored. The module collects real-time vibration data from electric motors. Vibration analyses in the temporal and frequency domains were carried out in both modules and gateways to compare their capabilities. This approach is also a springboard to using edge/fog computing to save cloud resources. A system prototype has been tested in the laboratory and in an industrial dairy plant. The results show that the proposed system can be used for continuous monitoring of any rotatory machine with similar accuracy to professional monitoring devices but at a significantly lower cost.

The edge computing model is an approach to Internet of Things (IoT) architectures based on the redistribution of services and infrastructure from centralized clouds to locations closer to IoT devices. The Web of Things (WoT) is another important IoT trend, currently led by the W3C, which aims at solving the IoT interoperability problem by adopting proven technologies and patterns from the Web. The design and validation of IoT deployments based on these paradigms is a complex task that involves multiple services, heterogeneous hardware and diverse communication technologies. Testing such projects in real world conditions usually requires a significant investment of resources. There are simulation tools that can assist in this process with much lower barriers of entry, however, they involve the designer making modelling assumptions that are not always representative of the real systems. This work presents an emulation tool for IoT projects based on the edge computing model that is able to seamlessly scale horizontally by leveraging container orchestration (Docker swarm mode). Furthermore, the W3C WoT model is included as a first-class citizen, enabling the designer to model all actors in the system as Things. The tool can run the real production code with minimal modifications and provides meaningful insights into the behaviour of the proposed architecture. This knowledge serves to rapidly iterate the optimization process, simplifying design issues and the detection of bottlenecks before committing to a real deployment in the field. A real-world scenario is also emulated in order to demonstrate its capabilities and validate its contribution.

The use of interactive multimedia communications is widespread in business and education, providing an enriched user experience in teleconferencing, distance learning and online meetings. Overlays networks are the underlying mechanisms enabling multipoint communication in interactive multimedia platforms. They must cope with constraints on heterogeneity, scalability and availability of communications, making their deployment and management a true challenge. Thus, several issues might affect the resilience of overlay networks, threatening ongoing communications. Overlay networks usually use self-organization and self-stabilization techniques to achieve resilient communications. Techniques implementing other self-management properties such as self-regulation also help in improving the resilience of the overlay. In this paper, the resilience of a reflector-based overlay network is improved with a self-regulation technique based on media transcoding. This balances the workloads of the reflectors of the overlay avoiding excessive usage of network links. As a result, the overlay is more resilient to failures. Extensive simulations have been carried out, and various media transcoding schemes have been compared, proving that self-regulation techniques improve the resilience of the reflector-based overlay network.

Synchronous e-training is emerging as an alternative for developing human resources training plans in large organizations. Real-time communications are used to emulate face-to-face interaction that occurs in on-campus learning environments. However, the security concerns that a synchronous e-training platform must face may compromise the integrity, availability and confidentiality of corporate information, which may lead to serious economic and legal consequences. The disclosure of corporate information or the unauthorized participation in e-training activities must be prevented. In this paper, the security issues in synchronous e-training are identified, and the threats to a real e-training platform are analyzed. The platform is organized into four virtual networks with different security requirements and vulnerabilities. The platform assumes that multicast communications are available in the underlying corporate network. The threats affecting each element of the platform and their impact on e-training activities are discussed. Finally, a security scheme is proposed fixing the aforementioned vulnerabilities. Digital certificates and encryption algorithms solve most of the vulnerabilities, but other techniques such as access control lists and user skills on security basics are essential. Most of the proposed scheme is applicable to other real-time communication systems, since the e-training platform is built using standard technologies commonly used in voice over IP systems.

Synchronous e-learning tools always include an audio-conference feature so participants in e-learning sessions can communicate orally. Audio quality is critical to avoid misunderstandings and improve user experience. Therefore, it is important to evaluate the audio quality that speech codecs can provide. Few existing assessment works consider the resources consumed (CPU or bitrate) to provide the audio quality, although synchronous e-learning sessions usually involve various participants, making resource consumption an important issue. In this paper, both objective and subjective audio quality measurement methods are used to characterize and estimate the audio quality of twenty speech codecs as a function of the resources consumed during synchronous e-learning sessions. Although users' opinions on audio quality are often more pessimistic than the evaluation provided by objective measurements, the correlation between objective and subjective measurements is high for medium-quality codecs. Users perceive lower audio quality for low-quality codecs than indicated by objective measurements, while they are not able to identify high-quality codecs, scoring them similarly to medium-quality codecs.

Synchronous e-training is emerging as an alternative for developing human resources training plans in large organizations. However, synchronous e-training platforms must face many issues affecting their resilience. Self-recovering and self-protection software techniques should be implemented to enable the continuity of e-training activities faced with network failures or entities of the platform going down. In this paper, the resilience issues in synchronous e-training are identified, and those presented in a real e-training platform are analyzed. The platform includes a self-optimizing technique to make efficient use of network resources as participants join and leave. Furthermore, various self-recovering and a self-protection techniques are proposed, making possible the continuity of ongoing e-training activities with reduced functionality until the automatic full system recovery. Most of the proposed techniques are applicable to other real-time communication systems, since the e-training platform is built using standard technologies such as SIP and RTP, commonly used in voice over IP systems.

A key issue in any system for distribution of continuous media-on-demand is the capacity of the system with regard to quality of the service specifications, that is, the number of simultaneous streams the system can provide until degradation of quality of reproduction and interactivity perceived by the users. This work presents the evaluation of interactive video-on-demand systems, with special attention to limitations caused by the video server. The evaluation is based on a set of metrics designed to determine the number of streams a video server can support under specific requirements of quality of the service as perceived by the users. To validate the utility of these metrics, a prototype of a news-on-demand service has been built and the load for this kind of systems has been characterised. In addition, a load generator which emulates the concurrent access of several users to the system has been built. The evaluation establishes the relationship between the video server limitations and the quality of the service perceived by the users.

Real-time distribution of continuous media on demand is a natural step in the evoluation of internet services. Here, we briefly present an interactive talks-on-demand service currently integrated in a digital newspaper. Low cost, high availability and high performance are features of the system supporting the service.