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Machine tools cannot produce accurate parts if performance degradation due to wear in their subsystems (e.g., spindle units) is not identified and con-trolled. Appropriate maintenance actions delay possible deterioration and mini-mize machining system stoppage time that leads to lower productivity and higher production cost. Measuring and monitoring machine tool condition has become increasingly important because of the introduction of agile production and in-creased requirements for product accuracy. Condition Based Maintenance (CBM) techniques, such as vibration monitoring, are becoming a very attractive method for companies operating high-value machines and components. One of the most common problems of rotating equipment, such as machine tool spindle units, is the condition of bearings. Vibration analysis can diagnose bearing damage by measuring the overall vibration of a spindle or, more precisely, by high-frequency techniques such as enveloping. This paper focuses on the use of vibration analysis to monitor and analyse the condition of machine tool spindle units. The method is a case study at a manufacturing company in Sweden. CBM, using vibration moni-toring, is implemented on different types of machine tools. The results of the im-plementation, as well as a vibration analysis of a spindle unit and its cost effec-tiveness, are presented in the paper.

Vibration analysis and the Shock Pulse Method (SPM) are two of the most popular condition monitoring techniques used in Condition-Based Maintenance (CBM) policy, especially for rotating equipment. To illustrate the extent to which advanced CBM techniques (in this case, vibration analysis and SPM) are applicable and cost effective in a manufacturing company, a pilot project was followed in real time. The pilot project was performed at a large manufacturing site in Sweden. The purpose of the project was to implement online condition monitoring of five critical gas circulation fans in the hardening process of the manufacturing company. This paper presents some of the main findings of the online condition monitoring of the fans for a period of three years. Consequently, based on the empirical data, the company was able to gain great profit due to preventing production losses by preventing breakdowns of the fans.

Machining systems (i.e., machine tools, cutting processes and their interaction) cannot produce accurate parts if performance degradation due to wear in their subsystems (e.g., feed-drive systems and spindle units) is not identified, monitored and controlled. Appropriate maintenance actions delay the possible deterioration and minimize/avoids the machining system stoppage time that leads to lower productivity and higher production cost. Moreover, measuring and monitoring machine tool condition has become increasingly important due to the introduction of agile production, increased accuracy requirements for products and customers' requirements for quality assurance. Condition Based Maintenance (CBM) practices, such as vibration monitoring of machine tool spindle units, are therefore becoming a very attractive, but still challenging, method for companies operating high-value machines and components. CBM is being used to plan for maintenance action based on the condition of the machines and to prevent failures by solving the problems in advance as well as controlling the accuracy of the machining operations. By increasing the knowledge in this area, companies can save money through fewer acute breakdowns, reduction in inventory cost, reduction in repair times, and an increase in the robustness of the manufacturing processes leading to more predictable manufacturing. Hence, the CBM of machine tools ensures the basic conditions to deliver the right ability or capability of the right machine at the right time. One of the most common problems of rotating equipment such as spindles is the bearing condition (due to wear of the bearings). Failure of the bearings can cause major damage in a spindle. Vibration analysis is able to diagnose bearing failures by measuring the overall vibration of a spindle or, more precisely, by frequency analysis. Several factors should be taken into consideration to perform vibration monitoring on a machine tool's spindle. Some of these factors are as follows: the sensor type/sensitivity, number of sensors to be installed on the spindle in different directions, positioning of the vibration accelerometers, frequency range to be measured, resonance frequency, spindle rotational speed during the measurements, measurement condition, including the no-load condition with tool clamped or without a tool, measuring tools and technologies, automatic or manual run of measurement, measurement routine, warning limits, and data handling and analysis, among other factors. The aim of this paper is thus to address CBM and particularly the implementation in the manufacturing industries focusing on the use of vibration monitoring techniques to monitor the condition of the machine tools' spindle units. To conduct this study, a pilot project was followed in real time. The pilot project was performed at a manufacturing company in Sweden. The company's product is gearboxes for the automotive industry, with a production volume of approximately 135,000 units per year. CBM, by online and off-line condition monitoring, using vibration monitoring, has been implemented on different types of machine tools, including horizontal and vertical turning machines, multi-task milling machines and grinding machines.

This article demonstrates the feasibility of the convolutional neural network (CNN) and pulse transit time (PTT)-based approach in estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP). Electrocardiogram (ECG) and photoplethysmography (PPG) signals were employed to calculate the PTT, which is the time delay between the R-wave peak Rof ECG, and specific points of the PPG waveforms. Then, the Blood pressure (BP), which is inversely related to PTT was estimated. A total of 22 patients with available ECG, PPG and SBP data were selected from the Medical Information Mart for Intensive Care (MIMIC III) dataset to validate the proposed model. A window of five minutes of recoding was chosen for each patient. Duration of each cardiac cycle was around 0.6 s, centred at R-peaks and sampled at 125 Hz. A CNN-based model was developed with four convolutional layers. The results showed that the average root mean square error (RMSE) of 5.42 mmHg and 7.81 mmHg were achieved for SBP and DBP, respectively.

This paper presents an introductory review of CBM practice applied in manufacturing industry, focusing the technical constituents within condition monitoring. The empirical base for the study is a case study of two major manufacturing sites in Sweden, complemented with a brief benchmark of data from two reference manufacturing sites from France and Brazil. The data from the main studies were collected by interviews and document analysis. The result indicates that there is a wide range within current practice of applying CBM. The implementations are dependent on process type (machining, assembly or heat treatment) and product type. By analysing the empirical data, gaps and challenges for implementing CBM in industry are presented, primarily focusing condition monitoring within manufacturing industry. The paper concludes with a discussion on possible future trends and research areas, needed to increase the industrial use of CBM.

This paper addresses the controversial gap between the environmental perspective and the cost perspective in a manufacturing context. The results of an empirical study on the heat treatment and phosphating processes performed by a manufacturing company indicate that implementing condition-based maintenance contributes not only to cost savings by preventing production losses and reducing equipment downtime but also to a more efficient use of resources by avoiding the generation of scraps and material wastage.