scholarly journals The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Dileep Kumar Soother ◽  
Jawaid Daudpoto ◽  
Nicholas R. Harris ◽  
Majid Hussain ◽  
Sanaullah Mehran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Diagnostic Techniques ◽  
Diagnostic Methods ◽  
Future Directions ◽  
Diagnosis And Prognosis ◽  
Feature Processing ◽  
Tremendous Progress ◽  
Motor Condition ◽  
Processing Techniques

The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and is also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for the effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that they may effectively contribute toward the implementation of DL models as applied to motor condition monitoring.

scholarly journals Peri-Implantitis Diagnosis and Prognosis Using Biomarkers in Peri-Implant Crevicular Fluid: A Narrative Review

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 214 ◽  
Author(s):  
Hatem Alassy ◽  
Praveen Parachuru ◽  
Larry Wolff
Keyword(s):  
Disease Progression ◽  
Gingival Crevicular Fluid ◽  
Current Knowledge ◽  
Current Model ◽  
Diagnostic Techniques ◽  
Diagnostic Methods ◽  
Future Directions ◽  
Diagnosis And Prognosis ◽  
Crevicular Fluid ◽  
Periodontal Probing

Dental implant diseases, peri-implantitis (PI) and peri-implant mucositis (PIM), have shown wide prevalence in recent studies. Despite the prevalence, diagnosing peri-implant disease (PID) remains challenging as common diagnostic methods of periodontal probing and radiographs may be inaccurate. These methods only document pre-existing destruction rather than current disease activity. Furthermore, there is no current model to predict the progression of PID. Though a predictive model is lacking, biomarkers may offer some potential. Biomarkers are commonly used in medicine to objectively determine disease state, or responses to a therapeutic intervention. Gingival crevicular fluid (GCF) biomarkers have moderate diagnostic validity in periodontitis. Biomarkers in peri-implant crevicular fluid (PICF) also show promising results in regard to their diagnostic and prognostic value. The aim of this review is to summarize the current knowledge of PICF biomarkers in the diagnosis of PID and evaluate their validity to predict disease progression. This review found that PICF studies utilize different methods of sampling and interpretation with varying validity (sensitivity and specificity). A number of promising diagnostic techniques were identified. Commercially available chair-side tests for MMP-8 to diagnose periodontal disease and PID activity are now available. Future directions include proteomics and metabolomics for accurate, site-specific diagnosis and prediction of PID progression. Although more research is needed, this review concludes that the assessment of proinflammatory cytokines (IL-1β, TNFα, MMP-8) in the PICF may be of value to diagnose PI and PIM but current research remains insufficient to indicate whether biomarkers predict peri-implant disease progression.

scholarly journals Solar Panel Detection within Complex Backgrounds Using Thermal Images Acquired by UAVs

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6219
Author(s):  
Jhon Jairo Vega Díaz ◽  
Michiel Vlaminck ◽  
Dionysios Lefkaditis ◽  
Sergio Alejandro Orjuela Vargas ◽  
Hiep Luong
Keyword(s):  
Deep Learning ◽  
Edge Detection ◽  
Diagnostic Methods ◽  
Detection Methods ◽  
Support Vector ◽  
Solar Panels ◽  
Low Contrast ◽  
Whole Process ◽  
Imaging Sensors ◽  
Processing Techniques

The installation of solar plants everywhere in the world increases year by year. Automated diagnostic methods are needed to inspect the solar plants and to identify anomalies within these photovoltaic panels. The inspection is usually carried out by unmanned aerial vehicles (UAVs) using thermal imaging sensors. The first step in the whole process is to detect the solar panels in those images. However, standard image processing techniques fail in case of low-contrast images or images with complex backgrounds. Moreover, the shades of power lines or structures similar to solar panels impede the automated detection process. In this research, two self-developed methods are compared for the detection of panels in this context, one based on classical techniques and another one based on deep learning, both with a common post-processing step. The first method is based on edge detection and classification, in contrast to the second method is based on training a region based convolutional neural networks to identify a panel. The first method corrects for the low contrast of the thermal image using several preprocessing techniques. Subsequently, edge detection, segmentation and segment classification are applied. The latter is done using a support vector machine trained with an optimized texture descriptor vector. The second method is based on deep learning trained with images that have been subjected to three different pre-processing operations. The postprocessing use the detected panels to infer the location of panels that were not detected. This step selects contours from detected panels based on the panel area and the angle of rotation. Then new panels are determined by the extrapolation of these contours. The panels in 100 random images taken from eleven UAV flights over three solar plants are labeled and used to evaluate the detection methods. The metrics for the new method based on classical techniques reaches a precision of 0.997, a recall of 0.970 and a F1 score of 0.983. The metrics for the method of deep learning reaches a precision of 0.996, a recall of 0.981 and a F1 score of 0.989. The two panel detection methods are highly effective in the presence of complex backgrounds.

scholarly journals Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhibin Zhao ◽  
Jingyao Wu ◽  
Tianfu Li ◽  
Chuang Sun ◽  
Ruqiang Yan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Open Source ◽  
Health Management ◽  
Prognostics And Health Management ◽  
Current Development ◽  
Diagnosis And Prognosis ◽  
Tremendous Progress ◽  
Challenges And Opportunities ◽  
Important Position

AbstractPrognostics and Health Management (PHM), including monitoring, diagnosis, prognosis, and health management, occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry. With the development of artificial intelligence (AI), especially deep learning (DL) approaches, the application of AI-enabled methods to monitor, diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry. However, there is still a gap to cover monitoring, diagnosis, and prognosis based on AI-enabled methods, simultaneously, and the importance of an open source community, including open source datasets and codes, has not been fully emphasized. To fill this gap, this paper provides a systematic overview of the current development, common technologies, open source datasets, codes, and challenges of AI-enabled PHM methods from three aspects of monitoring, diagnosis, and prognosis.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

Forensic research of staff fraud

2020 ◽  
Vol 23 (5) ◽  
pp. 585-600
Author(s):  
V.A. Timchenko
Keyword(s):  
Effective Means ◽  
Criminal Activity ◽  
Diagnostic Techniques ◽  
Scientific Basis ◽  
Diagnostic Methods ◽  
Structural Units ◽  
Forensic Research ◽  
Economic Information ◽  
Commercial Organizations ◽  
Original Approach

Subject. This article deals with the issues of forensic diagnostics, which is an effective means of detecting, preventing and suppressing staff fraud. Objectives. The article aims to present an original approach to the development of methods of forensic diagnosis of staff fraud based on the modeling method. It is also intended to identify a structure of staff fraud patterns and justify the need to classify the staff fraud methods. Methods. For the study, I used the methods of comparative analysis, systematization, induction, and deduction. Results. The article defines approaches to the formation of diagnostic methods of staff fraud and presents typical inconsistencies that arise in economic information under the influence of fraudulent actions of staff. It describes some diagnostic techniques that can detect staff fraud elements that occur in certain ways of criminal activity. Conclusions and Relevance. The proposed original approach helps develop standard and specific methods for diagnosing staff fraud on a scientific basis. The provisions outlined in the article can serve as a basis for scholarly discussion, contribute to the effectiveness of research on counter-fraud in the field of personnel fraud, and can be applied to the practical activities of structural units and individuals whose task is to combat staff fraud in commercial organizations.

In the Search of the Vulnerable Plaque: Current Diagnostic Techniques and Future Directions

2008 ◽  
Vol 4 (1) ◽  
pp. 21-29
Author(s):  
Manolis Vavuranakis ◽  
Theodore G. Papaioannou ◽  
Christodoulos Stefanadis
Keyword(s):  
Vulnerable Plaque ◽  
Diagnostic Techniques ◽  
Future Directions

scholarly journals Enteric Fever Diagnosis: Current Challenges and Future Directions

Pathogens ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 410
Author(s):  
Durga P. Neupane ◽  
Hari P. Dulal ◽  
Jeongmin Song
Keyword(s):  
Diagnostic Tests ◽  
Enteric Fever ◽  
Asymptomatic Carrier ◽  
Diagnostic Methods ◽  
Effective Control ◽  
Future Directions ◽  
Clinical Presentations ◽  
Global Spread ◽  
Life Threatening ◽  
Pros And Cons

Enteric fever is a life-threatening systemic febrile disease caused by Salmonella enterica serovars Typhi and Paratyphi (S. Typhi and S. Paratyphi). Unfortunately, the burden of the disease remains high primarily due to the global spread of various drug-resistant Salmonella strains despite continuous advancement in the field. An accurate diagnosis is critical for effective control of the disease. However, enteric fever diagnosis based on clinical presentations is challenging due to overlapping symptoms with other febrile illnesses that are also prevalent in endemic areas. Current laboratory tests display suboptimal sensitivity and specificity, and no diagnostic methods are available for identifying asymptomatic carriers. Several research programs have employed systemic approaches to identify more specific biomarkers for early detection and asymptomatic carrier detection. This review discusses the pros and cons of currently available diagnostic tests for enteric fever, the advancement of research toward improved diagnostic tests, and the challenges of discovering new ideal biomarkers and tests.

scholarly journals Tick-borne zoonoses and commonly used diagnostic methods in human and veterinary medicine

2021 ◽  
Author(s):  
Andrea Springer ◽  
Antje Glass ◽  
Julia Probst ◽  
Christina Strube
Keyword(s):  
Veterinary Medicine ◽  
Diagnostic Tests ◽  
One Health ◽  
Animal Health ◽  
Diagnostic Techniques ◽  
Diagnostic Methods ◽  
Diagnostic Tools ◽  
Zoonotic Pathogens ◽  
Health Concept ◽  
The One

AbstractAround the world, human health and animal health are closely linked in terms of the One Health concept by ticks acting as vectors for zoonotic pathogens. Animals do not only maintain tick cycles but can either be clinically affected by the same tick-borne pathogens as humans and/or play a role as reservoirs or sentinel pathogen hosts. However, the relevance of different tick-borne diseases (TBDs) may vary in human vs. veterinary medicine, which is consequently reflected by the availability of human vs. veterinary diagnostic tests. Yet, as TBDs gain importance in both fields and rare zoonotic pathogens, such as Babesia spp., are increasingly identified as causes of human disease, a One Health approach regarding development of new diagnostic tools may lead to synergistic benefits. This review gives an overview on zoonotic protozoan, bacterial and viral tick-borne pathogens worldwide, discusses commonly used diagnostic techniques for TBDs, and compares commercial availability of diagnostic tests for humans vs. domestic animals, using Germany as an example, with the aim of highlighting existing gaps and opportunities for collaboration in a One Health framework.

scholarly journals Special Issue “Advances in Machine Learning and Deep Learning Based Machine Fault Diagnosis and Prognosis”

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 532
Author(s):  
Mohand Djeziri ◽  
Marc Bendahan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Special Issue ◽  
Maintenance Strategies ◽  
Corrective Maintenance ◽  
Diagnosis And Prognosis ◽  
Machine Fault Diagnosis ◽  
Failure Prognosis ◽  
Fault Diagnosis And Prognosis

Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional ones [...]

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