Smart manufacturing is a part of the fourth industry revolution (Industry 4.0), which offers promising perspectives for high reliability, availability, maintainability, and safety production process. Indeed, smart monitoring methods, that are implemented in this kind of manufacturing process, allow efficient tracking of a system degradation in real time through appropriate sensors. Then, the sensor data are analyzed and processed to extract effective health indicators for fault detection, diagnostic and prognostics. This paper aims to develop a practical methodology for constructing a new health indicator based on heterogeneous sensor measurements to efficiently monitor system states. The proposed methodology is applied to extract the health indicator of a robot cutting tool (i.e. end-flat mill). This indicator is then used to diagnose the different fault types of the tool by an adaptive neuro-fuzzy inference system model.
World Health Organization (WHO) 2018 report says that 31% of global deaths are due to cardiovascular diseases. If no initiative is taken to check this most predictable and preventable among all chronic diseases, the world will have to suffer from billions of heart patients. India has the highest incidence of heart related diseases in the world and also it has one doctor for every 10,189 people though WHO suggests one doctor for 600 people [11]. The main aim of this work is to enable the doctors for early detection of cardiac disorder and save the life of heart patients. This work may be suitable for Tele-Cardiology application of Tele-Medicine which has a unique capacity to improve the health care service to millions of rural people. Cardiac arrhythmias classification has become an active research area over the last two decades. Further, from a literature survey, it is identified that finding an efficient feature scheme is a major challenge to enhance classification accuracy of cardiac disorders. Existing feature extraction methods may not be sufficient to detect the possibility of cardiac disorders as ECG is a quasi-periodic, nonlinear and non-stationary signal. Hence, in this paper, it is proposed to use a wavelet transform technique to extract ECG features. These features are used to classify seven types of cardiac arrhythmias (Cardiac Ischemia, Ventricular Tachycardia, Supra Ventricular Tachycardia, Ventricular Fibrillation, Atrial Fibrillation, Sudden Cardiac Arrest including Normal Sinus Rhythm). Multi-Layer Perceptron neural network classifier is proposed for this classification. Neural networks are strongly capable of learning and prediction which makes them an efficient tool to deal with nonlinear problems depending on their training data set size. With this proposed work, the classification accuracy is obtained as 98.8% and the medical statistics are obtained as Sensitivity (S) 97.6%, Positive Predictivity (Pp)-98.7% and Specificity (Sp)-99.7%. Further, with this efficient wavelet feature scheme, a Prototype Cardiac Alert System has been implemented using Arduino Uno and GSM SIM 900A for remote monitoring of the heart patients. A standard MIT-BIH ECG database, Waikato Environment for Knowledge Analysis (WEKA) and Matlab R2013a software have been used for the implementation.
Industry 4.0 is ushering in a new era of digitization, automation of machines and also provides autonomy in industrial processes. Despite these advances, the processes within order picking systems remain largely manual. Human cognitive and motor skills are difficult to replace and human flexibility is needed on the shop floor. It is well known that manual work requirements in intralogistics can lead to physical strain and in the long run to physical impairments such as musculoskeletal disorders. The aim of this article is to digitize the well-known ergonomics analysis tool Key Indicator Method (KIM) in order to obtain an automated evaluation and thus standardize the ergonomics assessment procedure in the future. For this purpose, a Motion Capture (MoCap) System will be used in a study where a manual picking process is simulated. With the help of this system, the course of a joint angle on the example of the spine during picking is analysed. The results are assessed on the basis of existing ergonomic standards and guidelines. At the same time, the KIM is performed by two ergonomics experts to validate the results of the MoCap system and to check the suitability of the system for ergonomic analysis.
