Automated anomaly recognition in real time data streams for oil and gas industry.

There is a growing demand for computer-assisted real-time anomaly detection - from the identification of suspicious activities in cyber security, to the monitoring of engineering data for various applications across the oil and gas, automotive and other engineering industries. To reduce the reliance on field experts' knowledge for identification of these anomalies, this thesis proposes a deep-learning anomaly-detection framework that can help to create an effective real-time condition-monitoring framework. The aim of this research is to develop a real-time and re-trainable generic anomaly-detection framework, which is capable of predicting and identifying anomalies with a high level of accuracy - even when a specific anomalous event has no precedent. Machine-based condition monitoring is preferable in many practical situations where fast data analysis is required, and where there are harsh climates or otherwise life-threatening environments. For example, automated conditional monitoring systems are ideal in deep sea exploration studies, offshore installations and space exploration. This thesis firstly reviews studies about anomaly detection using machine learning. It then adopts the best practices from those studies in order to propose a multi-tiered framework for anomaly detection with heterogeneous input sources, which can deal with unseen anomalies in a real-time dynamic problem environment. The thesis then applies the developed generic multi-tiered framework to two fields of engineering: data analysis and malicious cyber attack detection. Finally, the framework is further refined based on the outcomes of those case studies and is used to develop a secure cross-platform API, capable of re-training and data classification on a real-time data feed

Botnet detection in the Internet of Things using deep learning approaches.

The recent growth of the Internet of Things (IoT) has resulted in a rise in IoT based DDoS attacks. This paper presents a solution to the detection of botnet activity within consumer IoT devices and networks. A novel application of Deep Learning is used to develop a detection model based on a Bidirectional Long Short Term Memory based Recurrent Neural Network (BLSTM-RNN). Word Embedding is used for text recognition and conversion of attack packets into tokenised integer format. The developed BLSTM-RNN detection model is compared to a LSTM-RNN for detecting four attack vectors used by the mirai botnet, and evaluated for accuracy and loss. The paper demonstrates that although the bidirectional approach adds overhead to each epoch and increases processing time, it proves to be a better progressive model over time. A labelled dataset was generated as part of this research, and is available upon request

Individual Optimization of the Insertion of a Preformed Cochlear Implant Electrode Array

Purpose. The aim of this study was to show that individual adjustment of the curling behaviour of a preformed cochlear implant (CI) electrode array to the patient-specific shape of the cochlea can improve the insertion process in terms of reduced risk of insertion trauma. Methods. Geometry and curling behaviour of preformed, commercially available electrode arrays were modelled. Additionally, the anatomy of each small, medium-sized, and large human cochlea was modelled to consider anatomical variations. Finally, using a custom-made simulation tool, three different insertion strategies (conventional Advanced Off-Stylet (AOS) insertion technique, an automated implementation of the AOS technique, and a manually optimized insertion process) were simulated and compared with respect to the risk of insertion-related trauma. The risk of trauma was evaluated using a newly developed “trauma risk” rating scale. Results. Using this simulation-based approach, it was shown that an individually optimized insertion procedure is advantageous compared with the AOS insertion technique. Conclusion. This finding leads to the conclusion that, in general, consideration of the specific curling behaviour of a CI electrode array is beneficial in terms of less traumatic insertion. Therefore, these results highlight an entirely novel aspect of clinical application of preformed perimodiolar electrode arrays in general

Three-dimensional histological specimen preparation for accurate imaging and spatial reconstruction of the middle and inner ear

PURPOSE:    This paper presents a highly accurate cross-sectional preparation technique. The research aim was to develop an adequate imaging modality for both soft and bony tissue structures featuring high contrast and high resolution. Therefore, the advancement of an already existing microgrinding procedure was pursued. The central objectives were to preserve spatial relations and to ensure the accurate three-dimensional reconstruction of histological sections. METHODS:    Twelve human temporal bone specimens including middle and inner ear structures were utilized. They were embedded in epoxy resin, then dissected by serial grinding and finally digitalized. The actual abrasion of each grinding slice was measured using a tactile length gauge with an accuracy of one micrometre. The cross-sectional images were aligned with the aid of artificial markers and by applying a feature-based, custom-made auto-registration algorithm. To determine the accuracy of the overall reconstruction procedure, a well-known reference object was used for comparison. To ensure the compatibility of the histological data with conventional clinical image data, the image stacks were finally converted into the DICOM standard. RESULTS:    The image fusion of data from temporal bone specimens’ and from non-destructive flat-panel-based volume computed tomography confirmed the spatial accuracy achieved by the procedure, as did the evaluation using the reference object. CONCLUSION:    This systematic and easy-to-follow preparation technique enables the three-dimensional (3D) histological reconstruction of complex soft and bony tissue structures. It facilitates the creation of detailed and spatially correct 3D anatomical models. Such models are of great benefit for image-based segmentation and planning in the field of computer-assisted surgery as well as in finite element analysis. In the context of human inner ear surgery, three-dimensional histology will improve the experimental evaluation and determination of intra-cochlear trauma after the insertion of an electrode array of a cochlear implant system

Crack Healing and Mechanical Properties of Bacteria-based Self-healing Cement Mortar

In this study, the improvement of mechanical properties and crack healing as a result of the calcium carbonate precipitation due to bacterial activity have been investigated in two phases. First, the optimum mix design of self-healing cement mortar has been achieved considering different amounts and concentrations of the bacterial solution of bacterium Sporosarcina pasteurii (ATCC 11859) in non-pre-cracked specimens. Some of the mechanical properties, such as compressive strength, flexural strength, energy absorption capability, and weight change in bacteria added cement mortar specimens are compared with those of control specimens. Second, using the determined optimum mix design, mechanical properties of self-healing cement mortar specimens with initial cracks are compared with those of non-pre-cracked specimens to evaluate the recovery degree. 28-day compressive and flexural strengths of cement mortar specimens through direct addition of bacterial suspension with a concentration of 5.1 × 107 cells/ml improved by 45% and 18%, respectively. These results for 7-day specimens were 78% and 24%, respectively. Experimental flexural strengths of pre-cracked specimens are higher than their theoretical values based on the reduced cross-sections, and in pre-cracks with smaller dimensions, higher recovery degrees are achieved

Experimental visualization of labyrinthine structure with optical coherence tomography

Introduction: Visualization of inner ear structures is a valuable strategy for researchers and clinicians working on hearing pathologies. Optical coherence tomography (OCT) is a high-resolution imaging technology which may be used for the visualization of tissues. In this experimental study we aimed to evaluate inner ear anatomy in well-prepared human labyrinthine bones. Materials and Methods: Three fresh human explanted temporal bones were trimmed, chemically decalcified with ethylenediaminetetraacetic acid (EDTA), and mechanically drilled under visual control using OCT in order to reveal the remaining bone shell. After confirming decalcification with a computed tomography (CT) scan, the samples were scanned with OCT in different views. The oval window, round window, and remnant part of internal auditory canal and cochlear turn were investigated. Results: Preparation of the labyrinthine bone and visualization under OCT guidance was successfully performed to a remaining bony layer of 300μm thickness. OCT images of the specimen allowed a detailed view of the intra-cochlear anatomy. Conclusion: OCT is applicable in the well-prepared human inner ear and allows visualization of soft tissue parts.DFG/EXC/Hearing4allDFG/MA 4038/3-2Institute of Mechatronic System (IMES) OCT II/OR 196/17-

Generic application of deep learning framework for real-time engineering data analysis.

The need for computer-assisted real-time anomaly detection in engineering data used for condition monitoring is apparent in various applications, including the oil and gas, automotive industries and many other engineering domains. To reduce the reliance on domain-specific experts' knowledge, this paper proposes a deep learning framework that can assist in building a versatile anomaly detection tool needed for effective condition monitoring. The framework enables building a computational anomaly detection model using different types of neural networks and supervised learning. While building such a model, three types of ANN units were compared: a recurrent neural network, a long short-term memory network, and a gated recurrent unit. Each of these units has been evaluated on two benchmark public datasets. The experimental results of this comparative study revealed that the LSTM network unit that uses the sigmoid activation function, the Mean Absolute Error as the objective Loss function and the Adam optimizer as the output layer showed the best performance and attained the accuracy of over 77 % in detecting anomalous values in the datasets. Having determined the best performing combination of the neural network components, a computational anomaly detection model was built within the framework, which was successfully evaluated on real-life engineering datasets comprising the timeseries datasets from an offshore installation in North Sea and another dataset from the automotive industry, which enabled exploring the anomaly classification capability of the proposed framework

Evolving ANN-based sensors for a context-aware cyber physical system of an offshore gas turbine.

An adaptive multi-tiered framework, that can be utilised for designing a context-aware cyber physical system to carry out smart data acquisition and processing, while minimising the amount of necessary human intervention is proposed and applied. The proposed framework is applied within the domain of offshore asset integrity assurance. The suggested approach segregates processing of the input stream into three distinct phases of Processing, Prediction and Anomaly detection. The Processing phase minimises the data volume and processing cost by analysing only inputs from easily obtainable sources using context identification techniques for finding anomalies in the acquired data. During the Prediction phase, future values of each of the gas turbine's sensors are estimated using a linear regression model. The final step of the process - Anomaly Detection - classifies the significant discrepancies between the observed and predicted values to identify potential anomalies in the operation of the cyber physical system under monitoring and control. The evolving component of the framework is based on an Artificial Neural Network with error backpropagation. Adaptability is achieved through the combined use of machine learning and computational intelligence techniques. The proposed framework has the generality to be applied across a wide range of problem domains requiring processing, analysis and interpretation of data obtained from heterogeneous resources

Force measurement at the insertion process of cochlear implant electrodes

Several research groups have reported studies on the insertion force measurement at different cochlear implant electrodes. So far, all experimental setups to measure the forces applied to the electrode outside the cochlea (inner ear), ie have measured externally. Our aim was to integrate the sensors into an automatically operating instrument insertion, so that the forces can be measured, which act directly on the electrode, ie an internal force measurement

Toward automated cochlear implant insertion using tubular manipulators

During manual cochlear implant electrode insertion the surgeon is at risk to damage the intracochlear fine-structure, as the electrode array is inserted through a small opening in the cochlea blindly with little force-feedback. This paper addresses a novel concept for cochlear electrode insertion using tubular manipulators to reduce risks of causing trauma during insertion and to automate the insertion process. We propose a tubular manipulator incorporated into the electrode array composed of an inner wire within a tube, both elastic and helically shaped. It is our vision to use this manipulator to actuate the initially straight electrode array during insertion into the cochlea by actuation of the wire and tube, i.e. translation and slight axial rotation. In this paper, we evaluate the geometry of the human cochlea in 22 patient datasets in order to derive design requirements for the manipulator. We propose an optimization algorithm to automatically determine the tube set parameters (curvature, torsion, diameter, length) for an ideal final position within the cochlea. To prove our concept, we demonstrate that insertion can be realized in a follow-the-leader fashion for 19 out of 22 cochleas. This is possible with only 4 different tube/wire sets. © 2016 SPIE