Machine Health Monitoring and Fault Diagnosis Techniques Review in Industrial Power-Line Network

The machinery arrangements in industrial environment normally consist of motors of diverse sizes and specifications that are provided power and connected with common power-bus. The power-line could be act as a good source for travelling the signal through power-line network and this can be leave a faulty symptom while inspection of motors. This influence on other neighbouring motors with noisy signal that may present some type of fault condition in healthy motors. Further intricacy arises when this type of signal is propagated on power-line network by motors at different slip speeds, power rating and many faulty motors within the network. This sort of convolution and diversification of signals from multiple motors makes it challenging to measure and accurately relate to a certain motor or specific fault. This chapter presents a critical literature review analysis on machine-fault diagnosis and its related topics. The review covers a wide range of recent literature in this problem domain. A significant related research development and contribution of different areas regarding fault diagnosis and traceability within power-line networks will be discussed in detail throughout this chapter

Autopolypectomy of a Vocal Cord Polyp

Introduction Vocal cord polyps commonly occur in those with a history of vocal abuse. Patients with large lesions generally undergo microlaryngeal surgery under general anaesthesia. This unique case report highlights a strange scenario where the patient coughed out a fleshy mass during his morning walk and which was later confirmed as a vocal cord polyp.  Case Report A 62 year old male with a history of hoarseness of voice for 3 months presented to the ENT OPD holding a chunk of tissue which was apparently coughed out by him during his morning walk. After the incident, his symptoms had immediately improved. A videolaryngoscopy showed a congested spot on the right vocal cord being the probable site of origin of the lesion. On Histopathological examination, the tissue was reported as a vocal cord polyp.  The patient was managed conservatively but the lesion recurred at the same site after a month for which a microlaryngeal excision was performed. Discussion Vocal cord polyps are fairly common in ENT practice and usually present to the clinic with hoarseness of voice. Polyps that are small are usually managed conservatively by voice therapy alone whereas large polyps require surgical excision. This unique case report highlights a strange clinical scenario where the patient coughed out a large vocal cord polyp (Auto-polypectomy) during a bout of acute cough. This event saved him a surgery at the first instance, but eventually had a recurrence and had to undergo an excision under GA

Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

In this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature Analysis (MCSA) method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN) learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor

Numerical Treatment of Non-Linear System for Latently Infected CD4+T Cells: A Swarm- Optimized Neural Network Approach

Swarm-inspired computing techniques are the best candidates for solving various nonlinear problems. The current study aims to exploit the swarm intelligence technique known as Particle Swarm Optimization (PSO) for the numerical investigation of a nonlinear system of latently infected CD4+T cells. The strength of the Mexican Hat Wavelet (MHW) based unsupervised Feed Forward Artificial Neural Network (FFANN) is used to solve the nonlinear system of latently infected CD4+T cells. The function approximation of unsupervised ANN is used to construct the mathematical model of the latently infected CD4+T cells by defining the error function in the mean square manner. The adjustable parameters called the unknowns of the network are optimized by using the Particle Swarm Optimization (PSO), Nedler Mead Simplex Method (NMSM), and their hybrid PSO-NMSM. The PSO applied for the global optimization of weights aided by the NMSM algorithm for rapid local search. Finally, a Comprehensive Monte Carlo simulation and statistical analysis of the analytical method, numerical Range Kutta (RK) method, ANN optimized with Genetic Algorithm (GA) aided with Sequential Quadratic Programming (SQP) known as GA-SQP, ANN-PSO-SQP and the proposed MHW-HIVFFANN-PSO-NMSM are performed to validate the effectiveness, stability, convergence, and computational complexity of each scheme. It is observed that the proposed MHW-FFANN-HIVPSO-NMSM scheme has converged in all classes at 10 −6 , 10−7 , and 10 −8 and solved the nonlinear system of latently infected CD4+ T cells more accurately and effectively. The absolute error lies in 10−3 , 10−4 , 10−4 , and 10−5 for numerical, ANN-GA-SQP, ANN-PSO-SQP, and proposed MHW-ANN-PSO-NMSM respectively. Moreover, the proposed scheme is stable for the large number of independent runs. The values for global statistical indicators’ global mean squared error are lies 8.15E-09, 3.25E-10, 4.15E-09, and 3.15E-10 for class X(t), W(t), Y(t), and V(t) respectively whereas the global mean absolute deviation lies in range 7.35E-09, 8.50E-10, 2.10E-10 and 7.10E-09

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Smart sensor network organization: sensor data fusion and industrial fault traceability

The industrial environment usually contains multiple motors that are supplied through a common power bus. The power-line acts as a good conducting environment for signals to travel through the power-line network. In effect, this influences other motors with noisy signals that may indicate a fault condition. Further complexity arises when signals are generated by motors with different power ratings, a different slip speed and more than one source of fault signals. This sort of complexity and mixed signals from multiple sources makes them difficult to measure and precisely correlate to a given machine or fault. Generally, an industrial power-line network consists of different sizes of induction motor from small to large, which together can have a considerable combined influence on the overall system’s operation. The combined effect of all these induction motors can have a strong impact on power-line network permanence. In this thesis, the concept of cross evaluation of motor fault signals is considered to be signal propagation manifesting into healthy signal. Different concepts relating to propagation and manifestation of faults will be discussed and analysed. Initially, a systematic technique was employed to analyse the influence of the fault electric current signals of different motors within a power-line network. Further analysis analysed the attenuation ratio of electrical signals that leads toward a technical framework which evaluates the strength of signal propagation over a power-line network. The diagnostic process was demonstrated at individual sensing points to estimate the strength of propagated signals and identify fault points. This proved very helpful in maximizing the different independent observations. A sample industrial distributed motor network was simulated, to observe the behavior of a distributed power-line network in the presence of fault components. The multi-motor dynamic simulation model was developed, to compare the results with the test-bed practical results, to validate the acquired data. A number of case scenario experiments was done to verify the simulation results and validate the accuracy of these results. In this research, analytical results present significant improvements in describing the interference of faulty signals amongst motors running parallel to the power-line network. Some shortcomings were observed while implementing the strategy of distributed fault diagnosis, including false identification of similar types of fault symptom in power-line network and failure of the diagnosis system due to interference from non-linear noisy signals travelling within multi motor network. Some of these complications are supposed to be solvable by using an efficient and proper knowledge-based numerical technique. Furthermore, the focus of this research was also to develop a wireless node-level feature extraction technique for data fusion, using MCSA at end node-level. Decision-level fusion was implemented at the node coordinator for efficient fault diagnosis. In conclusion, this research does not claim to provide a complete solution to cover all types of fault diagnosis in electric drives. But it is a fitting attempt to provide a more reliable industry solution for motor fault diagnosis

Efficient natural language classification algorithm for detecting duplicate unsupervised features

This paper focuses on capturing the meaning of Natural Language Understanding (NLU) text features to detect the duplicate unsupervised features. The NLU features are compared with lexical approaches to prove the suitable classification technique. The transfer-learning approach is utilized to train the extraction of features on the Semantic Textual Similarity (STS) task. All features are evaluated with two types of datasets that belong to Bosch bug and Wikipedia article reports. This study aims to structure the recent research efforts by comparing NLU concepts for featuring semantics of text and applying it to IR. The main contribution of this paper is a comparative study of semantic similarity measurements. The experimental results demonstrate the Term Frequency–Inverse Document Frequency (TF-IDF) feature results on both datasets with reasonable vocabulary size. It indicates that the Bidirectional Long Short Term Memory (BiLSTM) can learn the structure of a sentence to improve the classification

BCAS: A Blockchain Model for Collision Avoidance to Prevent Overtaking Accidents on Roads

Overtaking at high speeds, especially on non-divided roadways, is a leading cause of traffic accidents. During overtaking maneuvers, humans are more likely to make mistakes due to factors that cannot be predicted. For overtaking operations in autonomous vehicles, prior research focused on image processing and distant sensing of the driving environment, which didn't consider the speed of the surrounding traffic, the size of the approaching vehicles, or the fact that they could not see beyond impediments in the road. The past researches didn't focus on the speed of the surrounding traffic or the size of the approaching vehicles. Moreover, most of the techniques were based on single agent systems where one agent manages the source vehicle's (autonomous) mobility within its surroundings. This research conducts a feasibility study on a remote Vehicle-to-Vehicle (V2V) communication framework based on Dedicated Short-Range Communication (DSRC) to improve overtaking safety. This work also tries to improve safety by introducing a blockchain-based safety model called BCAS (Blockchain-based Collision Avoidance System). The proposed multi-agent technique strengthens the ability of real-time, high-speed vehicles to make decisions by allocating the total computation of processing responsibilities to each agent. From the experimental results, it is concluded that the proposed approach performs better than existing techniques and efficiently covers the limitations of existing studies

BCAS: A Blockchain Model for Collision Avoidance to Prevent Overtaking Accidents on Roads

Overtaking at high speeds, especially on non-divided roadways, is a leading cause of traffic accidents. During overtaking maneuvers, humans are more likely to make mistakes due to factors that cannot be predicted. For overtaking operations in autonomous vehicles, prior research focused on image processing and distant sensing of the driving environment, which didn't consider the speed of the surrounding traffic, the size of the approaching vehicles, or the fact that they could not see beyond impediments in the road. The past researches didn't focus on the speed of the surrounding traffic or the size of the approaching vehicles. Moreover, most of the techniques were based on single agent systems where one agent manages the source vehicle's (autonomous) mobility within its surroundings. This research conducts a feasibility study on a remote Vehicle-to-Vehicle (V2V) communication framework based on Dedicated Short-Range Communication (DSRC) to improve overtaking safety. This work also tries to improve safety by introducing a blockchain-based safety model called BCAS (Blockchain-based Collision Avoidance System). The proposed multi-agent technique strengthens the ability of real-time, high-speed vehicles to make decisions by allocating the total computation of processing responsibilities to each agent. From the experimental results, it is concluded that the proposed approach performs better than existing techniques and efficiently covers the limitations of existing studies

Autopolypectomy of a Vocal Cord Polyp

Introduction Vocal cord polyps commonly occur in those with a history of vocal abuse. Patients with large lesions generally undergo microlaryngeal surgery under general anaesthesia. This unique case report highlights a strange scenario where the patient coughed out a fleshy mass during his morning walk and which was later confirmed as a vocal cord polyp.  Case Report A 62 year old male with a history of hoarseness of voice for 3 months presented to the ENT OPD holding a chunk of tissue which was apparently coughed out by him during his morning walk. After the incident, his symptoms had immediately improved. A videolaryngoscopy showed a congested spot on the right vocal cord being the probable site of origin of the lesion. On Histopathological examination, the tissue was reported as a vocal cord polyp.  The patient was managed conservatively but the lesion recurred at the same site after a month for which a microlaryngeal excision was performed. Discussion Vocal cord polyps are fairly common in ENT practice and usually present to the clinic with hoarseness of voice. Polyps that are small are usually managed conservatively by voice therapy alone whereas large polyps require surgical excision. This unique case report highlights a strange clinical scenario where the patient coughed out a large vocal cord polyp (Auto-polypectomy) during a bout of acute cough. This event saved him a surgery at the first instance, but eventually had a recurrence and had to undergo an excision under GA