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When induction motors are considered, there is no specific cost model for net savings per year due to condition-based maintenance (CBM) covering various parameters such as downtime, energy, quality, etc. The purpose of this paper is to develop a cost model for the financial viability of the implementation of CBM for induction motors.

A literature review has been carried out to identify the existing failure modes of motor, available condition monitoring techniques, the usefulness of CBM and different maintenance models available. Then, a cost model considering all parameters has been proposed.

A cost model has been proposed for the maintenance of induction motors. Method for the economic evaluation of the model has also been suggested in the paper. The application of the model has been illustrated through a case study of a steel plant, which suggests that investment in the condition monitoring of induction motors increases the net profit of the organization.

The proposed model is specifically designed for induction motors. All the motors under consideration are assumed to be of the same specifications, and fault in any motor is supposed to have the same effect on quality, cost, criticality, etc., of the operation and end product.

This paper will help the maintenance manager in decision making when maintenance action has to be carried out for a given motor under CBM for the better utilization of the equipment and resources. This paper also shows how to compute ROI on CBM investment.

The paper provides a cost model for the economic evaluation of implementing CBM for induction motors which will be useful to researchers and maintenance managers in effective decision making and maintenance planning. The methodology and the cost models are the original contribution of the authors.

The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.

The purpose of this study is to explore the use of smartphone-embedded microelectro-mechanical sensors (MEMS) for accurately estimating rotating machinery speed, crucial for various condition monitoring tasks. Rotating machinery (RM) serves a crucial role in diverse applications, necessitating accurate speed estimation essential for condition monitoring (CM) tasks such as vibration analysis, efficiency evaluation and predictive assessment.

This research explores the utilization of MEMS embedded in smartphones to economically estimate RM speed. A series of experiments were conducted across three test setups, comparing smartphone-based speed estimation to traditional methods. Rigorous testing spanned various dimensions, including scenarios of limited data availability, diverse speed applications and different smartphone placements on RM surfaces.

The methodology demonstrated exceptional performance across low and high-speed contexts. Smartphones-MEMS accurately estimated speed regardless of their placement on surfaces like metal and fiber, presenting promising outcomes with a mere 6 RPM maximum error. Statistical analysis, using a two-sample t-test, compared smartphone-derived speed outcomes with those from a tachometer and high-quality (HQ) data acquisition system.

The research limitations include the need for further investigation into smartphone sensor calibration and accuracy in extremely high-speed scenarios. Future research could focus on refining these aspects.

The societal impact is substantial, offering cost-effective CM across various industries and encouraging further exploration of MEMS-based vibration monitoring.

This research showcases an innovative approach using smartphone-embedded MEMS for RM speed estimation. The study’s multidimensional testing highlights its originality in addressing scenarios with limited data and varied speed applications.

Assessing the severity of failure modes of critical industrial machinery is often considered as an onerous task and sometimes misinterpreted by shop-floor engineer/maintenance personnel. The purpose of this paper is to develop an improved FMECA method for prioritizing the failure modes as per their risk levels and validating the same through a real case study of induction motors used in a process plant.

This paper presents a novel hybrid multi-criteria decision-making (MCDM) approach to prioritize different failure modes according to their risk levels by combining analytical hierarchy process (AHP) with a newly introduced MCDM approach, election based on relative value distance (ERVD). AHP is incorporated in the proposed approach to determine the criteria weights, evaluated in linguistic terms by industrial expert. Furthermore, ERVD, which is based on the concept of prospect theory of human cognitive process, is applied to rank the potential failure modes.

It is found that the proposed FMECA approach provides better results in accordance with the actual industrial scenario and helps in effectively prioritizing the failure modes. A comparison is also made to highlight the differences of results between the proposed approach with TOPSIS and conventional FMECA.

This research paper proposes an improved FMECA method and, thus, provides a deep insight to maintenance managers for effectively prioritizing the failure modes. The correct prioritization of failure modes will help in effective maintenance planning, thus reducing the downtime and improving profit to the organization.

A real case of process plant induction motor has been introduced in the research paper to show the applicability of this decision-making approach, and the approach is found to be suitable in correct prioritization of the failure modes.

Severity has been decoupled into various factors affecting it, to make it more relevant as per actual industrial scenario. Then, a novel modified FMECA has been developed using a hybrid MCDM approach (AHP and ERVD). This hybrid method, as well as its application in FMECA, has not been developed by any previous researcher. Moreover, the same has been thoroughly explained by considering a real case of process plant induction motors and validated with cross-functional experts.

Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and isolate the faults at the earliest possible opportunity becomes a complex decision-making process that often requires experts’ opinions and judicious decisions. The purpose of this paper is to propose a framework to detect, isolate and to suggest appropriate maintenance tasks for large-scale complex machinery (i.e. gearboxes of steel processing plant) in a simplified and structured manner by utilizing the prior fault histories available with the organization in conjunction with case-based reasoning (CBR) approach. It is also demonstrated that the proposed framework can easily be implemented by using today’s graphical user interface enabled tools such as Microsoft Visual Basic and similar.

CBR, an amalgamated domain of artificial intelligence and human cognitive process, has been applied to carry out the task of fault detection and isolation (FDI).

The equipment failure history and actions taken along with the pertinent health indicators are sufficient to detect and isolate the existing fault(s) and to suggest proper maintenance actions to minimize associated losses. The complex decision-making process of maintaining such equipment can exploit the principle of CBR and overcome the limitations of the techniques such as artificial neural networks and expert systems. The proposed CBR-based framework is able to provide inference with minimum or even with some missing information to take appropriate actions. This proposed framework would alleviate from the frequent requirement of expert’s interventions and in-depth knowledge of various analysis techniques expected to be known to process engineers.

The CBR approach has demonstrated its usefulness in many areas of practical applications. The authors perceive its application potentiality to FDI with suggested maintenance actions to alleviate an end-user from the frequent requirement of an expert for diagnosis or inference. The proposed framework can serve as a useful tool/aid to the process engineers to detect and isolate the fault of large-scale complex machinery with suggested actions in a simplified way.

The purpose of this paper is to focus on conducting accelerated life tests on aluminium electrolytic capacitors under accelerated temperature and voltage stress to study the effect of applied voltage and ambient temperature on the capacitor, its degradation over time, failure data collection, analysis and then modelling the failure times. Principles of DOE are used for studying the effect of temperature and voltage.

Life tests are conducted at three levels of temperature and applied voltage and the life of capacitor is ascertained at each treatment level. Life variation with voltage and temperature is studied to gain an insight as to how these factors affect the lifetime of the capacitor. The interaction effect of temperature and voltage on capacitor life is also established.

The life of the capacitor decreases exponentially with temperature and voltage at all the three factor levels. Ambient temperature, applied voltage and their interaction effect significantly affects the life of the capacitor. Applied voltage has the greatest effect followed by ambient temperature and then their interaction effect. Life of the capacitor has been estimated as 4,206 hrs when only voltage is taken as the accelerated stress using Inverse Power Law and as 4,003 hrs when both temperature and voltage are taken as accelerating stress using combination model.

This work consider only decrease in capacitance as the failure criterion. However, as a future scope, it is proposed that test may be conducted by taking into consideration not only the decrease in capacitance as the failure criteria but by monitoring all the performance parameters of the capacitor. This would give a more realistic assessment of life as it is possible that capacitor may have failed much before it reached the lower threshold capacitance value.

This work has lots of practical implications. It shows how DOE approach can be used for ALT data analysis and identification and effect of critical stresses acting on capacitors in real practice. Most critical types of stresses affecting the reliability can thus be controlled to ensure better performance. Product manufactures as well as users will be benefited by such findings. The paper has also illustrated how failure data can generated by degradation analysis using life test data collection at discrete intervals.

The methodology presents an alternative non traditional approach of accelerated life testing, which does not require continuous monitoring of test items. This only requires intermittent monitoring which reduces the need of test resources. Though the degradation study itself is not new but using degradation study for ALT data generation is new. This approach may considerably reduce the test duration and resources used for ALT. DOE approach gives more tangible result to study the effect of various variables on the dependent variable. As DOE approach uses a fractional factorial design, it can be very helpful to conduct life tests with minimum number of test units (only a fraction of full factorial test units). This will considerably reduce the test duration, resources requirement for testing, easier but accurate data analysis, and faster product development, especially when ALT is to be conducted at several stresses simultaneously.

The purpose of this paper is to propose a novel hybrid approach called as Markov System Dynamics (MSD) approach which combines the Markov approach with system dynamics (SD) simulation approach for availability modeling and to study the dynamic behavior of repairable systems.

In the proposed approach the identification of the single unit repairable system all possible states has been performed by using the Markov approach. The remaining stages of traditional Markov analysis are highly mathematically intensive. The present work proposes a hybrid approach called as MSD approach which combines the Markov approach with SD simulation approach to overcome some of the limitations of Markov process in a simple and efficient way for availability modeling and to study the dynamic behavior of this system.

The proposed framework is illustrated for a single unit repairable system. The worked out example shows the steady state point and also it gives the point, interval and steady state availabilities and also the dynamic behavior of the system. However this methodology can be extended easily for more complex multi-state maintainable systems. The results of the simulation when compared with that obtained by traditional Markov analysis clearly validate the proposed approach as an alternative approach for availability modeling of repairable systems.

In many practical situations we require to find the time at which our system reaches steady state conditions for planning maintenance activities. The proposed MSD method in this paper is capable of finding this steady state point very easily.

The proposed approach clearly indicates the time at which the system reaches its steady state and calculates the point, interval availabilities for planning maintenance activities. The different parties, i.e., engineers and machine operators, can jointly work with this model in order to understand the dynamic behavior of repairable systems.

Technological advancements and growing complexity of many real-time systems, namely, communication, transportation, defense systems, etc., necessitate the importance to adopt a well-planned repair process such as phase type quasi-renewal process contributing to an improved system performance. Further, in an attempt to boost the role of maintenance as a financial benefactor, repairman’s multiple vacation policy is incorporated. Also, the significance of the degree of repair is illustrated while indicating the suitability of the matrix-analytic approach via the phase type quasi-renewal operating/repair times in reliability. The paper aims to discuss these issues.

The optimal replacement policy is obtained by employing the matrix-analytic method and minimum average cost rate.

The considered models make a significant contribution towards establishing that the matrix-analytic method, using the phase type quasi-renewal process, aids in reducing the computations and also fills the gap in the literature in the study of deteriorating systems. Availability and rate of occurrence of failures are evaluated in transient and steady-state regime.

This model differs from the existing models, in that, a repairman’s multiple vacation, delayed repair time and representation of the failure occurrence by a mixed Poisson process have been incorporated into the analysis. Also, time-dependent case and N-policy have been adopted to explore the optimality issues using phase type quasi-renewal process analytically. The numerical illustrations warrant that the maintenance policy proposed in this paper produces a considerably lower cost.

This paper aims to simulate vital corrective actions (CAs) affecting system availability through a system dynamics approach based on the results obtained by analyzing the causal relationships among failure modes and effects analysis elements.

A stock and flow diagram has been developed to simulate system behaviors during a timeframe. Some improvement scenarios regarding the most necessary CAs according to their strategic priority and the possibility of eliminating root causes of critical failure modes in a roller-transmission system have been simulated and analyzed to choose the most effective one(s) for the system availability. The proposed approach has been examined in a steel-manufacturing company.

Results indicated the most effective CAs to remove or diminish critical failure causes that led to the less reliability of the system. It illustrated the impacts of the selected CAs on eliminating or decreasing root causes of the critical failure modes, lessening the system’s failure rate and increasing the system availability more effectively.

Results allow managers and decision-makers to consider different maintenance scenarios without wasting time and more cost, choosing the most appropriate option according to system conditions.

This study innovation would be the dynamic analysis of interactions among failure modes, effects and causes over time to predict the system behavior and improve availability by choosing the most effective CAs through improvement scenario simulation via VENSIM software.

Discusses the relationship between plant availability and maintenance expenditure and their limiting features. Achieving higher plant availability always necessitates a higher maintenance budget and may not be economically feasible in many cases. Through a mathematical modelling the variation of net income with respect to plant availability has been studied and the limiting availability values have been established. Expressions for optimum availability and maintenance cost have also been obtained. An illustrative example has been worked out.