The use of condition monitoring can be seen as a development from preventive maintenance, which itself developed from break down maintenance. Modern process requirements demand greater availability and reliability of machines which can only be provided through accurate monitoring of machine health. This allows maintenance personnel to determine the best possible course of action based on knowledge available from condition monitoring (Mahamad, 2010). Condition monitoring has found greater favour in maintenance circles based on savings and system simplification provided by it. Not only does condition monitoring allow the operator to make correct and on time maintenance decisions, it also allows a reduction in maintenance costs. The improvements offered in terms of greater system availability also provide direct financial benefit to processes that cannot afford to have significant maintenance delays. Overall a sizable reduction in maintenance costs and direct fiscal benefits offered by more reliable machines has pushed condition monitoring to the forefront of maintenance globally (Fuqing, 2011). Background Condition monitoring can be carried out in a number of different ways ranging from the manual tabulation of manually measured variables to more complex and intelligent systems that offer diagnosed causes for machine wear. Over the years, condition monitoring has evolved significantly given the need to diagnose faults in larger and more dynamic industrial systems. There has been an increase in the use of artificial intelligence and a number of mathematical techniques, such as principal component analysis (PCA), in order to isolate faults and offer diagnosis for industrial systems. Need for Artificial Intelligence (AI) Applications in Condition Monitoring AI techniques have been applied to a number of different industrial systems including condition monitoring. It must be recognised that the application of conventional techniques such as time domain, frequency domain and envelope analysis do not always yield satisfactory fault detection. In order to drive up the reliability of the fault detection mechanisms, AI and PCA are applied. More notably, neural networks and fuzzy logic have found pervasive application in condition monitoring systems. The application of AI for condition monitoring is required in areas where analytical knowledge is difficult to come across. The use of AI allows creation of new knowledge from existing knowledge and input data from monitored variables (Shi, 2004). The use of AI and PCA techniques is required since vibration data sets contain a lot of data which results in the creation of a large set of features. Optimal feature selection is only achievable through the application of IA and PCA. A comparison of IA and PCA application versus conventional methods such as time domain, frequency domain and envelope analysis reveals that the former results in greater efficiency and savings. The application of conventional methods requires human resources with the right expertise as well as significant time that cost the maintenance establishment significantly. In contrast, the application of IA and PCA techniques allows for much faster and more reliable fault detection without the hassle of added costs. However, it has to be kept in mind that variables measured
The Exploitation of Information Technologies for Machinery Condition Monitoring Introduction to Condition Monitoring Condition monitoring is carried out in various industries to ensure the reliability and state of machine health. Principally condition monitoring allows maintenance personnel to monitor machine health by measuring certain key variables that are related to machine element performance…
This article is a research-based project that aims to compare two or more open source Network Intrusion Detection Systems, in terms of their operation, methods of detection, capabilities, and performance. Network Intrusion Detection Systems (NIDSs), are developed to monitor network activities for any malicious activities and network violations.
In the PeakVue technique, the capturing of the accurate peak value diagnosed inside the time waveform related with each of the impacting events provides the analyst with information regarding severity of the damage too. PeakVue is a tool that can obtain the amplitude trend of maximum waveform and stress wave spectral frequency distribution; and these pieces of information substantially improve the quality of fault severity assessment (Robinson and Berry, 2001).
Various chemical and mechanical parameters affect the removal rate of CMP. Parameters such as velocity, pressure, slurry composition, and pH effects affect the rate of wear and tear of the pads used in this process. The above parameters will be studied in this project with an aim to determine optimal conditions for the process, hence design a sensor system that can be used to optimize the entire operation.
As Yu (1998) in his studies reflected, it is the responsibility of the controllers to detect and manage any irregularities obtained in the operations of the airline systems and thus take the necessary steps and measures such that the normal schedules of the airlines may be obtained.
It is fair to say that despite this research effort, fault localization in complex communication systems remains to bean open research problem.1 There are plenty of challenges in fault localization and complex communication systems in particular which have presented numerous proposed solutions from a lot of researchers in the course of the last ten years, all presenting certain advantages and shortcomings.
To prevent, foresee and detect mass attacks is one of the primary tasks of the modern programmers. Online systems are very important in the process of risks reduction. Online interaction attacks should be prevented and suppressed once they are detected. These challenges and problems in the computer and the Internet world are intensified by the existence of mass-attacks of users.
They offer support systems to the operations of humans. They need care and protection against both external and internal attacks. Collaborative attacks occur in a system where more than one running processes collaborate or synchronize their actions to disturb a system.
This approach tends to rely strongly on predictive and preventive maintenance rather than corrective maintenance since problems are discovered before a breakdown actually occurs (Jar dine & Lin, 2006). However, it needs to be kept in mind that such methods may not always be successful given the complex interaction between various levels of components in any practical mechanical system.
This dissertation reveals general view about some techniques to detect the liquid level especially non-contact ultrasonic sensors, compares between Optical liquid level sensors and Ultrasonic liquid level sensors in Riyadh wastewater treatment station and develops the liquid level detection system in wastewater treatment stations by using optical sensors.