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"Condition monitoring of machines in non-stationary operations (CMMNO) can be seen as the major challenge for research in the field of machinery diagnostics.Condition monitoring of machines in non-stationary operations is the title of the presented book and the title of the Conference held in Hammamet - Tunisia March 26 ? 28, 2012. It is the second conference under this title, first took place in Wroclaw - Poland , March 2011.The subject CMMNO comes directly from industry needs and observation of real objects. Most monitored and diagnosed objects used in industry works in non-stationary operations condition. The non-stationary operations come from fulfillment of machinery tasks, for which they are designed for. All machinery used in different kind of mines, transport systems, vehicles like: cars, buses etc, helicopters, ships and battleships and so on work in non-stationary operations.The papers included in the book are shaped by the organizing board of the conference and authors of the papers.The papers are divided into five sections, namely :Condition monitoring of machines in non-stationary operationsModeling of dynamics and fault in systemsSignal processing and Pattern recognitionMonitoring and diagnostic systems Noise and vibration of machinesThe presented book gives the back ground to the main objective of the CMMNO 2012 conference that is to bring together scientific community to discuss the major advances in the field of machinery condition monitoring in non-stationary conditions."

"This book is aimed at researchers, industry professionals and students interested in the broad ranges of disciplines related to condition monitoring of machinery working in non-stationary conditions. Each chapter, accepted after a rigorous peer-review process, reports on a selected, original piece of work presented and discussed at the International Conference on Condition Monitoring of Machinery in Non-stationary Operations, CMMNO 2018, held on June 20-22, 2018, in Santander, Spain. The book describes both theoretical developments and a number of industrial case studies, which cover different topics, such as: noise and vibrations in machinery, conditioning monitoring in non-stationary operations, vibro-acoustic diagnosis of machinery, signal processing, application of pattern recognition and data mining, monitoring and diagnostic systems, faults detection, dynamics of structures and machinery, and mechatronic machinery diagnostics."

"Condition monitoring of engineering plants has increased in importance as engineering processes are automated and manpower is reduced. However, electrical machinery receives attention only at infrequent intervals when plant is shut down and the application of protective relays to machines has also reduced operator surveillance.A first edition of Condition Monitoring of Electrical Machines, written by Tavner and Penman, was published in 1987. The economics of industry have now changed, as a result of the privatisation and deregulation of the energy industry, placing emphasis on the importance of reliable operation of plant, throughout the whole life cycle, regardless of first cost. The availability of advanced electronics and software in powerful instrumentation, computers, and digital signal processors (DSP) has simplified our ability to instrument and analyse machinery. As a result condition monitoring is now being applied to a wider range of systems from fault-tolerant drives of a few hundred watts in the aerospace industry, to machinery of a few hundred megawatts in major capital plant.In this new book the original authors have been joined by Ran, an expert in power electronics and control, and Sedding, an expert in the monitoring of electrical insulation systems. Together the authors have revised and expanded the earlier book, merging their own experience with that of machine analysts to bring it up to date."

"This book describes in detail different types of vibration signals and the signal processing methods, including signal resampling and signal envelope, used for condition monitoring of drivetrains. A special emphasis is placed on wind turbines and on the fact that they work in highly varying operational conditions. The core of the book is devoted to cutting-edge methods used to validate and process vibration data in these conditions. Key case studies, where advanced signal processing methods are used to detect failures of gearboxes and bearings of wind turbines, are described and discussed in detail. Vibration sensors, SCADA (Supervisory Control and Data Acquisition), portable data analyzers and online condition monitoring systems, are also covered. This book offers a timely guide to both researchers and professionals working with wind turbines (but also other machines), and to graduate students willing to extend their knowledge in the field of vibration analysis."

"Polymeric insulators are widely used now days due to some advantages such as compact design.light,handy and easy to instalk therefore less installatio cost....."

"Pumps have played a signiiicant role in industrial plants. Preventive monitoring techniques should be applied to pumps to minimise production loss. The vibration symptom generated from the pump was investigated as an indication of the cause Of the pump performance deterioration. This paper presents the experiment on two pumps with different health condition. The test rig was set up and the vibration analysis soiiware was developed for this purpose. Analysis was conducted on both performance evaluation and vibration signal interpretation from both of the pumps.Some key issues related with pump health assessment were discussed; these include pump efiiciency, cavitation, vane pass frequency, and statistical parameter."

"This book promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as, fuzzy systems, rough and neuro-rough sets, neural and Bayesian networks, hidden Markov and Gaussian mixture models, and support vector machines."

"Gas turbin engine akan mengalami degradasi sejalan dengan waktu menjadi perhatian utama perusahaan oil dan gas karena akan berpengaruh terhadap engine reliability, availability, dan maintenance cost.Time based maintenance mengabaikan kondisi performance gas turbin apakah dalam kondisi sehat atau rusak, tetap dilakukan overhaul jika telah tercapai TBO Time Between Overhaul antara 25.000 jam. Kerusakan pada gas turbin sebelum TBO sulit terdeteksi. Performance monitoring mampu mendeteksi degradasi gas turbin sehingga membantu penggunanya untuk beralih dari Time based ke Condition Based Maintenance. Dari pola degradasi gas turbin, dapat ditentukan prediksi sisa umur pakai dengan menggunakan metode regresi dan analisa resiko.

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Gas turbine will experience performance degradation align with operation time. The degradation of gas turbine performance will be main focus in oil and gas company since its affecting to the reliability, availability, and maintenance cost.Time based maintenance was ignoring gas turbine performance whether it still can be running or it will be failed, gas turbine overhaul is still carried out when running hours reached Time Between Overhaul TBO at 25.000 hours. Gas turbine failure before overhauling schedule is difficult to predict when time based maintenance strategy applied.

Performance monitoring can detect gas turbine degradation so that assist oil and gas company as the user to change their maintenance program from Time Based to Condition Based Maintenance.Based on gas turbine performance degradation pattern. It can be determined the remaining useful life of gas turbine prior to overhauling by using regression method and carry out risk analysis."

"ABSTRAKSistem monitoring kendaraan saat ini telah banyak diterapkan pada industri otomotif dalam bentuk dashboard yang mampu menampilkan berbagai indikator mengenai kondisi mobil. Akan tetapi, dashboard yang ada saat ini hanya menampilkan data mengenai kendaraan melalui port OBD saja. Untuk menguji kenyamanan dan keamanan kendaraan diperlukan tambahan sensor-sensor yang di pasang di berbagai tempat pada kendaraan. Agar dashboard kendaraan dapat menampilkan data yang di dapat dari sensor-sensor tersebut, digunakan sebuah perangkat yang berfungsi sebagai Main Unit Data Collector. Perangkat tersebut berperan untuk mengumpulkan data dari sensor dan port OBD. Data yang terkumpul kemudiam dikirimkan ke perangkat Android dengan metode socket programming. Dari penelitian yang dilakukan didapatkan hasil bahwa penggunaan Main Unit Data Collector yang dipadukan dengan metode socket programming sebagai protokol komunikasi mampu menghasilkan dashboard kendaraan yang menampilkan data tentang kondisi kendaraan secara lebih komprehensif dan real-time, dimana data mampu ditampilkan dengan sampling time sebesar 185.1454 milidetik.

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ABSTRACTThe vehicle monitoring system has now been widely applied to the automotive industry in the form of a dashboard that capable of displaying various indicators that give information about the condition of the car in realtime. However, the current dashboard only displays data about the vehicle through the OBD port only. To test the comfort and safety of the vehicle, additional sensors that are installed in various places on the vehicle are needed. In order to vehicles dashboard displays the data obtained from that other sensors, a Main Unit Data Collector device is used to collect sensor data and then send the data to an Android device. The data is sent by using socket programming method. From the research conducted it was found that the use of the Main Unit Data Collector that combined with the socket programming method used for its communication protocol can produce vehicle dashboards that display more comprehensive and real-time data, where is capable of displays data with a sampling time of 185.1454 milliseconds."