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The objective of this study is to develop an automated tool condition monitoring scheme for PCB drilling.

Vibration signals are used to distinguish micro drill wear stages with proper features extraction and classifier design. Then a tool condition monitoring system is built up through a back propagation neural network (BPNN).

Experimental results show that BPNN is a practical method of modeling tool wear, and with this method a tool condition monitoring system is built up using energy ratio, root mean square (RMS) and kurtosis coefficient that transformed by vibration signals.

In the further investigation, more signal samples should be computed as monitoring features for BPNN modeling. In addition, in order to build the best monitoring model, it is necessary to evaluate the performance of the BPNN model in advance, and optimize the process.

The paper provides a method and a system for PCB drill wear monitoring. The method and system can achieve on‐line monitoring of PCB drill condition.

Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of this paper is to describe how to identify degradation condition of rolling bearing and predict its fault time in big data environment in order to achieve zero downtime performance and preventive maintenance for the rolling bearing.

The degradation characteristic parameters of rolling bearings including intrinsic mode energy and failure frequency were, respectively, extracted from the pre-processed original vibration signals using EMD and Hilbert transform. Then, Spearman’s rank correlation coefficient and PCA were used to obtain the health index of the rolling bearing so as to detect the appearance of degradations. Furthermore, the degradation condition of the rolling bearings might be identified through implementing the monotonicity analysis, robustness analysis and degradation analysis of the health index.

The effectiveness of the proposed method is verified by a case study. The result shows that the proposed method can be applied to monitor the degradation condition of the rolling bearings in industrial application.

Further experiment remains to be done so as to validate the effectiveness of the proposed method using Apache Hadoop when massive sensor data are available.

The paper proposes a methodology for rolling bearing condition monitoring representing the steps that need to be followed. Real-time sensor data are utilized to find the degradation characteristics.

The result of the work presented in this paper form the basis for the software development and implementation of condition monitoring system for rolling bearings based on Hadoop.

The purpose of this paper is to demonstrate how Internet of Things (IoT) technology can enable highly distributed elevator equipment servicing by using remote-monitoring technology to facilitate a shift from traditional corrective maintenance (CM) and time-based maintenance (TBM) to more predictive, condition-based maintenance (CBM) in order to achieve various benefits.

Literature review indicates that CBM has advantages over conventional CM and TBM from a theoretical perspective, but it depends on continuous monitoring enhancement via advanced IoT technology. An in-depth case study was carried out to provide practical evidence that IoT enables elevator firms to achieve CBM.

From a theoretical perspective, the CBM of elevators makes business sense. The challenges lie in data collection, data analysis and decision making in real-world business contexts. The main findings of this study suggest that CBM can be commercialized via IoT in the case of elevators and would improve the safety and reliability of equipment. It would, thus, make sense from technological, process and economic perspectives.

Our longitudinal real-world case study demonstrates a practical way of making the CBM of elevators widespread. Integrating IoT and other advanced technology would improve the safety and reliability of elevator equipment, prolong its useful life, minimize inconvenience and business interruptions due to equipment downtime and reduce or eliminate major repairs, thus greatly reducing maintenance costs.

The main contribution of this paper lies in the empirical demonstration of the benefits and challenges of CBM via IoT relative to conventional CM and TBM in the case of elevators. The authors believe that this study is timely and will be valuable to firms working on similar research or commercialization strategies.

The purpose of this paper is to analyze the effects of condition-based maintenance based on unscheduled maintenance delays that were caused by ATA chapter 21 (air conditioning). The goal is to show the introduction of condition monitoring in aircraft systems.

The research was done using the Airbus In-Service database to analyze the delay causes, delay length and to check if they are easy to detect via condition monitoring or not. These results were then combined with delay costs.

Analysis shows that about 80 percent of the maintenance actions that cause departure delays can be prevented when additional sensors are introduced. With already existing sensors it is possible to avoid about 20 percent of the delay causing maintenance actions.

The research is limited on the data of the Airbus in-service database and on ATA chapter 21 (air conditioning).

The research shows that delays can be prevented by using existing sensors in the air conditioning system for condition monitoring. More delays can be prevented by installing new sensors.

The research focuses on the effect of the air conditioning system of an aircraft on the delay effects and the impact of condition monitoring on delays.

The purpose of this paper is to investigate the failure-driven capacity consumption of wheels on the track, to determine whether there are some relations to vehicle wheel configurations that show a larger amount of failures, and to ascertain the influence of the temperature and the travelling direction of the train on the number of events. This information can be used to develop prognostic health management so that more track capacity can be gained without modifications, re-building or re-investments.

This paper presents a study of 1,509 warning and alarm events concerning train wheels. The data come from the infrastructure manager's wheel defect detectors and wheel profile measurement system. These data have been analysed and processed to find patterns and connections to different vehicles, travelling directions and temperatures.

Lower temperatures increase the probability of wheels having high vertical forces. Trains with different wheel configurations show different results. With high vertical forces, the probability of wheel failures at axles 6 and 7 is high for locomotives with two bogies and three axles in each bogie (2×3). All these findings can be used to develop the maintenance, monitoring and inspection principles for wheels.

The inspection of wheels to detect failures needs to be more frequent on days and in seasons with lower temperatures. The wheel inspection should be performed more frequently at axles 6 and 7 for locomotives with a 2×3 wheel configuration. The inspection and monitoring of wheels need to be carried out more carefully for trains travelling south, to avoid a large amount of wheels with high force levels rolling in the southern direction.

The analysis carried out in this paper identifies important factors that correlate with the high occurrence of wheel defects. It also proposes a conceptual e-maintenance model for the combination of wheel condition monitoring data from different system. The value of this study is the provision of information to support prognostic and health management system to support proactive maintenance.

In manufacturing systems intelligent techniques are being used to integrate and interpret data from multiple sensors to predict tool wear and tool life. Less attention is devoted to developments of integrated condition monitoring systems, which enable the user to evaluate a multi‐variant system based on the data collected from, e.g. maintenance, quality, production, etc. In this paper we discussed different approaches of how to keep availability, quality and productivity at high levels. Also, we proposed a new approach for an expert system concept, which is characterised by using a total quality maintenance (TQMain) concept; having a common database, and a continuously improved knowledge base with an intelligent inference engine. It can enhance data reliability, decision making certainty, remove the redundancy in monitoring systems, and allow the user to detect and eliminate reasons behind variations through effective diagnosis and prognosis. This will enhance the performance‐efficiency, availability and quality rate, i.e. overall equipment effectiveness of the manufacturing systems.

The purpose of this paper is to present an exploratory study in order to understand the contributions of the resilience engineering (RE) concept and the use of unmanned aerial systems (UASs) technology to support the safety planning and control (SPC) process.

A case study on a construction project was conducted and involved the following steps: diagnosis of the SPC process; development of a safety monitoring protocol using UASs; and field tests to monitor safety performance using UASs and data analysis.

In terms of its theoretical contribution, this work presents a conceptual framework explaining how the RE and the UASs can contribute to the SPC process. Also, this paper provides, as a practical contribution, a protocol for safety monitoring with UASs integrated into the safety routine, highlighting the tasks that can be checked and unsafe conditions and safety/production conflicts identified through monitoring.

This study can be used to support and stimulate the construction managers who wish to adopt the RE concepts and UAS technology to improve safety management.

An efficient SPC process can improve the work conditions at construction sites, contributing with the reduction of accidents rates.

The paper highlights the need to adopt new approaches, as RE concepts and UAS technology to support the SPC process, in order to improve safety conditions at construction sites.

The purpose of this paper is to develop a comprehensive approach to efficiently integrate maintenance and operation by combining process and condition monitoring data with performance measures.

Intelligent stress, condition and health indicators have been developed for control and condition monitoring by combining generalised moments and norms with efficient nonlinear scaling. The data analysis resulting nonlinear scaling functions can also be used to handle performance measures used for management. The generalised norms provide limits for an advanced statistical process control.

The data‐driven analysis methodology demonstrates that management‐oriented indicators can be presented in the same scale as intelligent condition and stress indices. Control, condition monitoring, maintenance and performance monitoring are represented as interactive feedback loops.

Performance analysis can be based on real‐time information by using various stress, condition and health indices as inputs. Similar approaches can be used for outputs: quality indices, harmonised indices, key performance indicators, process capability indices and overall equipment effectiveness. Since consistent linguistic explanations based on nonlinear scaling are available for all these indices, the analysis can be further deepened with LE modelling. Efficient monitoring with intelligent indices provides a good basis for control and condition‐based maintenance and performance monitoring.

The paper extends the nonlinear scaling methodology and linguistic equations to intelligent performance measures. The methodology provides a consistent way to also represent all information with linguistic terms.

For many decades, it has been recognized that maintenance activities should be adapted to the specific usage of a system. For that reason, many advanced policies have been developed, such as condition-based and load-based maintenance policies. However, these policies require advanced monitoring techniques and rather detailed understanding of the failure behavior, which requires the support of an OEM or expert, prohibiting application by an operator in many cases. The present work proposes a maintenance policy that relieves the high (technical) demands set by these existing policies and provides a more accurate specification of the required (dynamic) maintenance interval than traditional usage-based maintenance.

The methodology followed starts with a review and critical assessment of existing maintenance policies, which are classified according to six different aspects. Based on the need for a technically less demanding policy that appears from this comparison, a new policy is developed. The consecutive steps required for this functional usage profiles based maintenance policy are then critically discussed: usage profile definition, monitoring, profile severity quantification and the possible extension to the fleet level. After the description of the proposed policy, it is demonstrated in three case studies on real systems.

A maintenance policy based on a simple usage registration procedure appears to be feasible, which enables a significantly more efficient maintenance process than the traditional usage-based policies. This is demonstrated by the policy proposed here.

The proposed maintenance policy based on functional usage profiles offers the operators of fleets of systems the opportunity to increase the efficiency and effectiveness of their maintenance process, without the need for a high investment in advanced monitoring systems and in experts interpreting the results.

The original contribution of this work is the explicit definition of a new maintenance policy, which combines the benefits of considering the effects of usage or environment severity with a limited investment in monitoring technology.

The purpose of this paper is to propose a model for assisting in the decision-making process for acquiring a condition monitoring (CM) system for an oil-immersed power transformer in order to improve its maintainability.

The proposed model is based on the analytic hierarchy process. The assessment was performed by pairwise comparisons, and a sensitivity analysis (what-if analysis) was used to identify the implications of changing the criteria weights. In order to select the criteria and alternatives, a search was conducted for the power transformer failure modes, monitored parameters and CM technologies.

The proposed model provides a structured solution for a complex problem: deciding the best combination of technologies for CM of power transformers.

Because the pairwise comparisons were done only by the author, the results may need to be improved with the assessment of more experts. Also, it was done for a specific type of transformer; it might be necessary to customise the alternatives for other cases. Finally, as a future consideration, more levels can be added to the hierarchy to improve the accuracy of the model.

The power transformer is an asset where the most appropriate maintenance strategy for it is condition-based maintenance. In order to improve its maintainability, it is recommendable to improve its testability and diagnosability. For achieving this goal, the maintenance personnel have to decide the best combination of technologies for CM. The methodology developed can assist the decision makers to select the most appropriate cost-benefit strategy.

The paper presents a structured and generic method of selecting the most appropriate CM system for power transformers.