Sequential fault detection for sealed deep groove ball bearings of in-wheel motor in variable operating conditions

Sealed deep groove ball bearings (SDGBBs) are employed to perform the relevant duties of in-wheel motor. However, the unique construction and complex operating environment of in-wheel motor may aggravate the occurrence of SDGBB faults. Therefore, this study presents a new intelligent diagnosis method for detecting SDGBB faults of in-wheel motor. The method is constructed on the basis of optimal composition of symptom parameters (SPOC) and support vector machines (SVMs). SPOC, as the objects of a follow-on process, is proposed to obtain from symptom parameters (SPs) of multi-direction. Moreover, the optimal hyper-plane of two states is automatically obtained using soft margin SVM and SPOC, and then using multi-SVMs, the system of intelligent diagnosis is built to detect many faults and identify fault types. The experiment results confirmed that the proposed method can excellently perform fault detection and fault-type identification for the SDGBB of in-wheel motor in variable operating conditions

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Systems Engineering: Availability and Reliability

Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling

High Fidelity Model of Ball Screws to Support Model-based Health Monitoring

L'abstract è presente nell'allegato / the abstract is in the attachmen

Deep Learning for predictive maintenance

Recently, with the appearance of Industry 4.0 (I4.0), machine learning (ML) within artificial intelligence (AI), industrial Internet of things (IIoT) and cyber-physical system (CPS) have accelerated the development of a data-orientated applications such as predictive maintenance (PdM). PdM applied to asset-dependent industries has led to operational cost savings, productivity improvements and enhanced safety management capabilities. In addition, predictive maintenance strategies provide useful information concerning the source of the failure or malfunction, reducing unnecessary maintenance operations. The concept of prognostics and health management (PHM) has appeared as a predictive maintenance process. PHM has become an unavoidable tendency in smart manufacturing to offer a reliable solution for handling industrial equipment’s health status. This later requires efficient and effective system health monitoring methods, including processing and analysing massive machinery data to detect anomalies and perform diagnosis and prognosis. Prognostics is considered a key PHM process with capabilities for predicting future states, mainly based on predicting the residual lifetime during which a machine can perform its intended function, i.e., estimating the remaining useful life (RUL) of a system. The prognostic research domain is far from being mature, which is still new and explains the various challenges that must be addressed. Therefore, the work presented in this thesis will mainly focus on the prognostic of monitored machinery from an RUL estimation point of view using Deep Learning (DL) algorithms. Capitalising on the recent success of the DL, this dissertation introduces methods and algorithms dedicated to predictive maintenance. We focused on improving the performance of aero-engine prognostic, particularly in estimating an accurate RUL using ensemble learning and deep learning. To this end, two contributions have been proposed, and the results obtained were validated by an extensive comparative analysis using public C-MAPSS turbofan engine benchmark datasets. The first contribution, for RUL predictions, we proposed two-hybrid methods based on the promising DL architectures to leverage the power of the multimodal and hybrid deep neural network in order to capture various information at different time intervals and ultimately achieve more accurate RUL predictions. The proposed end-to-end deep architectures jointly optimise the feature reduction and RUL prediction steps in a hierarchical manner, intending to achieve data representation in low dimensionality and minimal variable redundancy while preserving critical asset degradation information with minimal preprocessing effort. The second contribution, in a practical situation, RUL is usually affected by uncertainty. Therefore, we proposed an innovative RUL estimation strategy that assesses degrading machinery’s health status (provides the probabilities of system failure in different time windows) and provides the prediction of RUL window. Keywords: Prognostics and Health Management (PHM), Remaining useful life (RUL), Predictive Maintenance (PdM), C-MAPSS dataset, Ensemble learning, Deep learnin

Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures

In the past, when elements in sructures were composed of perishable materials, such as wood, the maintenance of houses, bridges, etc., was considered of vital importance for their safe use and to preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary concern. When it was realized that even for structures fabricated with these materials that the useful life has an end and that it was being approached, planning maintenance became an important and non-negligible aspect. Thus, the concept of structural health monitoring (SHM) was introduced, designed, and implemented as a multidisciplinary method. Computational mechanics, static and dynamic analysis of structures, electronics, sensors, and, recently, the Internet of Things (IoT) and artificial intelligence (AI) are required, but it is also important to consider new materials, especially those with intrinsic self-diagnosis characteristics, and to use measurement and survey methods typical of modern geomatics, such as satellite surveys and highly sophisticated laser tools

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018

Technology 2000, volume 1

The purpose of the conference was to increase awareness of existing NASA developed technologies that are available for immediate use in the development of new products and processes, and to lay the groundwork for the effective utilization of emerging technologies. There were sessions on the following: Computer technology and software engineering; Human factors engineering and life sciences; Information and data management; Material sciences; Manufacturing and fabrication technology; Power, energy, and control systems; Robotics; Sensors and measurement technology; Artificial intelligence; Environmental technology; Optics and communications; and Superconductivity