Remote online machine condition monitoring using advanced internet, wireless and mobile communication technologies

A conceptual model with wireless and mobile techniques is developed in this thesis for remote real-time condition monitoring, which is applied for monitoring, diagnosing, and controlling the working conditions of machines. The model has the following major functions: data acquisition, data processing, decision making, and remote communication. The data acquisition module is built up within this model using the sensory technique and data I/O interfaces to acquire the working conditions data of a machine and extract the physical information about the machine (e.g. failure, wear, etc.) for data processing and decision making. The data processing is conducted using digital conversion and feature extraction to process the received analogue condition data and convert the data into the physical quantities of working condition of the machine for sequent fault diagnosis. A real-time fault diagnostic scheme for decision-making is applied based on digital filtering and pattern classification to real-time identify the fault symptom of the machine and provide advice for decision making for maintenance. Process control is implemented to control the operation status of the machine automatically, inform the maintenance personnel diagnostic results and alert the working conditions of the machine. Remote communication with wireless and mobile features greatly advance the machine’s condition monitoring technology with real-time fault diagnostic capacity, by providing a wireless-based platform to enable the implementation of data acquisition, real-time fault diagnosis, and decision making through the Internet, wireless, and mobile phone network. The model integrating above techniques and methods has been applied into the following three areas: (1) Development of a Remote Real-time Condition Monitoring System of Industrial Gearbox, supported by the Stimulation Innovation Success programme (2007-2008); (2) Development of a Remote Control System of Solid Desiccant Dehumidifier for Air Conditioning in Low Carbon Emission Buildings, supported by the Sustainable Construction iNET programme (2009-2010); (3) Development of an Innovative Remote Monitoring System of Thermo-Electric-Generations, supported by the Sustainable Construction iNET programme (2010-2011). The combination of wireless and mobile techniques with data acquisition, real-time fault diagnosis, and decision-making, into a model for remote real-time condition monitoring is a novel contribution to this area

Optoelectronic devices and packaging for information photonics

This thesis studies optoelectronic devices and the integration of these components onto optoelectronic multi chip modules (OE-MCMs) using a combination of packaging techniques. For this project, (1×12) array photodetectors were developed using PIN diodes with a GaAs/AlGaAs strained layer structure. The devices had a pitch of 250μm, operated at a wavelength of 850nm. Optical characterisation experiments of two types of detector arrays (shoe and ring) were successfully performed. Overall, the shoe devices achieved more consistent results in comparison with ring diodes, i.e. lower dark current and series resistance values. A decision was made to choose the shoe design for implementation into the high speed systems demonstrator. The (1x12) VCSEL array devices were the optical sources used in my research. This was an identical array at 250μm pitch configuration used in order to match the photodetector array. These devices had a wavelength of 850nm. Optoelectronic testing of the VCSEL was successfully conducted, which provided good beam profile analysis and I-V-P measurements of the VCSEL array. This was then implemented into a simple demonstrator system, where eye diagrams examined the systems performance and characteristics of the full system and showed positive results. An explanation was given of the following optoelectronic bonding techniques: Wire bonding and flip chip bonding with its associated technologies, i.e. Solder, gold stud bump and ACF. Also, technologies, such as ultrasonic flip chip bonding and gold micro-post technology were looked into and discussed. Experimental work implementing these methods on packaging the optoelectronic devices was successfully conducted and described in detail. Packaging of the optoelectronic devices onto the OEMCM was successfully performed. Electrical tests were successfully carried out on the flip chip bonded VCSEL and Photodetector arrays. These results verified that the devices attached on the MCM achieved good electrical performance and reliable bonding. Finally, preliminary testing was conducted on the fully assembled OE-MCMs. The aim was to initially power up the mixed signal chip (VCSEL driver), and then observe the VCSEL output

Training quantum neural networks using the Quantum Information Bottleneck method

We provide in this paper a concrete method for training a quantum neural network to maximize the relevant information about a property that is transmitted through the network. This is significant because it gives an operationally well founded quantity to optimize when training autoencoders for problems where the inputs and outputs are fully quantum. We provide a rigorous algorithm for computing the value of the quantum information bottleneck quantity within error $\epsilon$ that requires $O(\log^2(1/\epsilon) + 1/\delta^2)$ queries to a purification of the input density operator if its spectrum is supported on $\{0\}~\bigcup ~[\delta,1-\delta]$ for $\delta>0$ and the kernels of the relevant density matrices are disjoint. We further provide algorithms for estimating the derivatives of the QIB function, showing that quantum neural networks can be trained efficiently using the QIB quantity given that the number of gradient steps required is polynomial.Comment: 34 pages, 1 figur

Plastic Optical Fiber Technology for Reliable Home Networking: Overview and Results of the EU Project POF-ALL

The rising performance of broadband connections for residential users, particularly in conjunction with fiber to the home, will present a new challenge for telecom operators in the short and medium terms: how to deliver the high bit rate digital signals with high quality-of-service to all consumer devices scattered inside the building of final users? Among the many different solutions for the home network, we review in this article the use of polymer optical fibers for short-reach and high-capacity optical communications for residential customer premises. POF is an easy-to-install, low-cost, and eye-safe solution for these networks, with the potential of being future-proof. In this article the state of the art in POF technology is presented by summarizing significant results achieved in the European project POF-ALL. Data transmission rates of more than 1 Gb/s over 50+ m and 100 Mb/s over 200+ m of standard step-index POF have been show

An Intelligent Mobility Prediction Scheme for Location-Based Service over Cellular Communications Network

One of the trickiest challenges introduced by cellular communications networks is mobility prediction for Location Based-Services (LBSs). Hence, an accurate and efficient mobility prediction technique is particularly needed for these networks. The mobility prediction technique incurs overheads on the transmission process. These overheads affect properties of the cellular communications network such as delay, denial of services, manual filtering and bandwidth. The main goal of this research is to enhance a mobility prediction scheme in cellular communications networks through three phases. Firstly, current mobility prediction techniques will be investigated. Secondly, innovation and examination of new mobility prediction techniques will be based on three hypothesises that are suitable for cellular communications network and mobile user (MU) resources with low computation cost and high prediction success rate without using MU resources in the prediction process. Thirdly, a new mobility prediction scheme will be generated that is based on different levels of mobility prediction. In this thesis, a new mobility prediction scheme for LBSs is proposed. It could be considered as a combination of the cell and routing area (RA) prediction levels. For cell level prediction, most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the New Markov-Based Mobility Prediction (NMMP) and Prediction Location Model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression and insufficient accuracy. In this thesis, Location Prediction based on a Sector Snapshot (LPSS) is introduced, which is based on a Novel Cell Splitting Algorithm (NCPA). This algorithm is implemented in a micro cell in parallel with the new prediction technique. The LPSS technique, compared with two classic prediction techniques and the experimental results, shows the effectiveness and robustness of the new splitting algorithm and prediction technique. In the cell side, the proposed approach reduces the complexity cost and prevents the cell level prediction technique from performing in time slots that are too close. For these reasons, the RA avoids cell-side problems. This research discusses a New Routing Area Displacement Prediction for Location-Based Services (NRADP) which is based on developed Ant Colony Optimization (ACO). The NRADP, compared with Mobility Prediction based on an Ant System (MPAS) and the experimental results, shows the effectiveness, higher prediction rate, reduced search stagnation ratio, and reduced computation cost of the new prediction technique

Benchmarking a many-core neuromorphic platform with an MPI-based DNA sequence matching algorithm

SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS)multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. Several studies have shown that neuromorphic platforms allow flexible and efficient simulations of SNN by exploiting the efficient communication infrastructure optimised for transmitting small packets across the many cores of the platform. However, the effectiveness of neuromorphic platforms in executing massively parallel general-purpose algorithms, while promising, is still to be explored. In this paper, we present an implementation of a parallel DNA sequence matching algorithm implemented by using the MPI programming paradigm ported to the SpiNNaker platform. In our implementation, all cores available in the board are configured for executing in parallel an optimised version of the Boyer-Moore (BM) algorithm. Exploiting this application, we benchmarked the SpiNNaker platform in terms of scalability and synchronisation latency. Experimental results indicate that the SpiNNaker parallel architecture allows a linear performance increase with the number of used cores and shows better scalability compared to a general-purpose multi-core computing platform

Many-core and heterogeneous architectures: programming models and compilation toolchains

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen677. INGEGNERIA INFORMATInopartially_openembargoed_20211002Barchi, Francesc

The Study of Wind Field ERA-20C in Monsoon Domains for Rainfall Predictor in Indonesia (Java, Sumatra, and Borneo)

In recent years, various research institutions have developed diverse global data reanalysis projects. This provides an opportunity to gain long-term of meteorological data for local scale. This study aims to select the potential predictor of wind fields u and v of the ERA-20C dataset, a reanalysis dataset, at 850 mb from seven domains or windows of Asian, Maritime Continent, Australian, and Western North Pacific monsoon related physically to rainfall anomaly patterns in Indonesia. The vector wind velocity scalar was obtained by using a Helmholtz decomposition to separate the total circulation v = (u,v) into the divergent component/velocity potential (χ) or Phi and rotational component/stream function (ψ) or Psi for obtaining the scalar variable of vector wind velocity. The method applied Singular value decomposition (SVD) to identify pairs of spatial patterns (expansion coefficients) between the predictors of Phi and Psi in seven domains, with rainfall data from 48 stations in Java, Sumatra, and Borneo Islands from 1981 to 2010. The results revealed that spatial patterns correlations of Java Islands were the highest in the Maritime Continent monsoon domain (80o−150o E and 15oS−15o N), while Sumatra and Borneo Island were in the Western North Pacific monsoon domain (100o–130o E and 5o–15o N) with predictor Psi. The lowest correlation for Java, Sumatra, and Borneo was the Australian monsoon domain (110o E–130o E and 5o S–15o S) with predictor Phi.  In general, spatial pattern correl-ations of Java Island were higher than others, agreeing with monsoonal rainfall type dominantly in the region