Purpose -The purpose of this paper is to provide a brief overview of condition based maintenance (CBM) with definitions of various terms, overview of some history, recent developments, applications, and research challenges in the CBM domain. Design/methodology/approach -The article presents the insight into various maintenance strategies and provides their respective merits and demerits in various aspects. It then provides the detailed discussion of CBM that includes applications of various methodologies and technologies that are being implemented in the field. Finally, it ends with open challenges in implementing condition based maintenance systems. Findings -This paper surveys research articles and describes how CBM can be used to optimize maintenance strategies and increase the feasibility and practicality of a CBM system. Practical implications -CBM systems are completely practical to implement and applicable to various domains including automotive, manufacturing, aviation, medical, etc. This paper presents a brief overview of literature on CBM and an insight into CBM as a maintenance strategy. CBM has wide applications in automotive, aviation, manufacturing, defense, and other industries. It involves various disciplines like data mining, artificial intelligence, and statistics to enable the systems to be maintenance intelligent. These disciplines help in predicting the future consequences based on the past and current system conditions. Based on the authors' studies, implementation of such a system is easy and cost effective because it uses existing subsystems to collect statistical data. On top of that it requires building a software layer to process the data and to implement the prognosis techniques in the form of algorithms. Social implications -The design of CBM systems highly impact the society in terms of maintenance cost (i.e. reduces the maintenance cost of automobiles, safety by providing real time reporting of the fault using prognosis). Originality/value -To the best of the authors' knowledge, this paper is first of its kind in the literature which presents several maintenance strategies and provides a number of possible research directions listed in open research challenges.

In this paper, we present the preliminary results of a new global three-dimensional (3-D) ionospheric model developed using artificial neural networks (ANNs) by assimilating long-term ionospheric observations from nearly two decades of ground-based Digisonde, satellite-based topside sounders, and global positioning system-radio occultation measurements. The present 3-D model is named ANN-based global 3-D ionospheric model (ANNIM-3D), which is the extension of previous work on the ANN-based two-dimensional ionospheric model by Sai Gowtam and Tulasi Ram (2017a,

In this paper, we have evaluated five prediction approaches from two disciplines for condition-based maintenance. It also includes a case study for vehicle tire pressure monitoring as an example application. Main focus of this paper is on two widely used areas in prediction: (i) statistics, (ii) neural networks. It is well known that these two areas have wide applications in forecasting. Statistical and neural network techniques are very powerful for predicting the future states based on current and previous states of the system or subsystem. Application of ARAR and Holt-Winters (HW) in CBM has been presented from the statistics point of view. On the other hand, application of focused time delay, linear predictor, and backpropagation neural network has also been presented to prove the robustness of statistical approaches. Paper presents detailed comparative simulation study to show the suitability and feasibility of all the techniques. We assumed that the sensors are directly mounted on tires externally and report the current tire pressure to control or analysis. The control unit performs tire pressure analysis and reports the decision to operator or intended group about current pressure as well as the impending pressure conditions. Finally, investigation ends with conclusion that HW is best suited among these five approaches for tire pressure prediction and could be useful to design a CBM application for any system.3.3.1. Neural network topologies and training. The neural networks exist mainly in two forms: static and dynamic. Static networks 28 are traditional input » process » output-based networks, also called feed-forward networks or memory-less networks. In such networks, input to a layer depends only on preceding layer. The static neural networks have wide applications in maintenance engineering and A. PRAJAPATI AND S. GANESAN The time-based maintenance 8 is a maintenance based on predefined intervals, no matter what is the condition at that time. For example, oil change in car is periodic either based on mileage or interval, significant portion of its life may be still left. B. Condition Based MaintenanceCondition Based maintenance 5 deals with monitoring the condition of mission critical and safety-critical parts in carrying out maintenance whenever necessary to avoid hazards rather than following a fixed schedule. C. Condition-Based Maintenance Plus (CBM+)The CBM + includes RCM analysis other than regular CBM component. Again air force definition of CBM + 5 is as follows, 'Condition Based Maintenance Plus (CBM+) expands upon these basic concepts, encompassing other technologies, processes, and procedures that enable improved maintenance and logistics practices. These future and existing technologies, processes, and capabilities will be addressed during the capabilities planning, acquisition, sustainment, and disposal of a weapon system.' D. Reliability Center MaintenanceRCM 2 enables the formulation of the maintenance strategy by selecting the right mix of corrective maintenance, scheduled based mainten...

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In this research project two solar still plants have been fabricated; single and double slope still plants. The exergy and exergy efficiency concepts have been applied for the comparative analyses of both solar stills. Experimental readings, i.e. various temperatures, amounts of distilled water and solar radiations have been taken for solar stills at different time intervals in Indore (22° 43′ 4.51″ N and 75° 49′ 59.88″ E). The percentage variations in the different parameters, i.e. increments in temperature, increments in quantity of water, thermal efficiencies, rates of exergy at inlet/outlet and exergy efficiencies have also been calculated for stills at various solar intensities and time intervals. After experimental and comparative analyses, better outputs have been shown at 14:50 PM for double slope and 14:45 PM for single slope plant due to the cumulative effects of solar radiations on the still plants. During this period thermal/exergy efficiencies, i.e. 46.630/1.949% and 47.259/3.389% were found the best. The performance of single slope solar still has been found better, and 14:30 to 14:50 PM time interval has been recorded superior for the distillation of water through still plants where percentage increments of the performance evaluating parameters were enhanced. Keywords Single and double slope solar stills • Thermal efficiency • Rates of inlet and outlet exergy • Exergy efficiency • Solar radiation List of symbols C Specific heat of water (J/kg K) dT w Temperature difference which was achieved due to solar energy (K) I b R B Solar radiation which is available for single and double slope solar still (W/m 2) L Length of the solar still (m) m Mass flow rate of pure water (kg/s) T 0, T w and T s Ambient, water and Sun's surface temperatures (K) W Width of the solar still (m) Greek symbols η Thermal efficiency of the solar still (%) α g , ρ g and τ g Absorptivity, reflectivity and transmissivity of the glass Ψ in and Ψ out Exergy at inlet and outlet for single and double slope solar still (W)