the failure mechanism and failure mode of solar PV modules and their impact on degradation in operating conditions. The RPN analysis was helpful to identify the single failure mode, which affected the system performance more like the hot spot, encapsulate de- lamination and the corrosion in solder bond fatigue.  They presented a general model to explain the functional relationship among the three factors of RPN and applied in model for demonstration and discussed the unique role of each factor for comparing the risk of different failure modes.  They extended the definition of RPN by multiplying it with a weight parameter which characterize the importance of the failure causes within the system. Finally, the effectiveness of the method was demonstrated with numerical examples. RPN technique was also applied and used in the automotive industry to prioritize their failure modes.  He studied a new methodology for Laboratory Assessment and Risk Analysis in research environment (LARA) and developed a new risk index called Laboratory Criticity Index (LCI) for risk ranking. LCI is conceived through two approaches which are the RiskPriorityNumber (RPN) and the Analytic Hierarchy Process (AHP) which provided the identification of critical areas and prioritization of safety actions.  They presented a research aimed to propose a new method called Total Efficient RiskPriorityNumber (TERPN) to classify risks and to identify corrective actions in order to obtain the highest risk reduction with the lowest cost. The main scope was to suggest a suitable model for ranking risks in a company to reach the maximum effectiveness of prevention and protection strategies. The TERPN method was an integration of the popular Failure Mode Effect and Criticality Analysis (FMECA) with other important factors in risk assessment. Moreover, RPN technique is also used to study cases in medical fields to
accordance with specialized issues: generation strategies that cause the failure and failure identification systems. Besides, extraordinary enterprises have diverse failures which are named repairable and hopeless, i.e. their expenses are differed. All the more vitally, once failures are not completely identified and disposed of before getting to shoppers, went with guarantee cost, pay cost for issues happened in utilizing the defective items/administrations would emerge; and even the imperceptible cost for business notoriety/brand would truly influence the execution of the entire association. These costs, hereinafter, are alluded in a more broad term as "Quality cost". Therefore, to cure the above downsides, this paper proposes incorporating the quality cost as an extra figure the routine RPN equation to improve its segregation control in dissecting failure modes and their belongings. Our proposed recipe is called "Modified RiskPriorityNumber" (MRPN) .
ANN (Artificial Neural Network) is one of AI (Artificial Intelligent) concept which applies machine learning method in its learning process. In this study, ANN-BP (Back Propagation) was used to predict the failure of cooling system based on RPN (RiskPriorityNumber). Adaptive learning is applied to improve the performance of ANN-BP. The aim of this study is to schedule maintenance of cooling systems based on predicted RPN results.
notoriety/brand would truly influence the execution of the entire association. These costs, hereinafter, are alluded in a more broad term as "Quality cost". Therefore, to cure the above downsides, this paper proposes incorporating the quality cost as an extra figure the routine RPN equation to improve its segregation control in dissecting failure modes and their belongings. Our proposed recipe is called "Modified RiskPriorityNumber" (MRPN) .
This system of “FMEA” can help people to manage the production process from the beginning of a process through the period of the production. It also helps to prevent problem or error from occurring or detect, and react to variation in the process before they materialize as product defect. The traditional FMEA determines the riskpriority of failure modes using the riskprioritynumber (RPN) by multiple the ranks of the three element of risk namely Severity (S), Occurrence (O), and Detection (D).The best action that can be taken for high risk problems (especially those have high RPN) are using mistake proofing technique or systems In addition, when the FMEA system is linked with the mistake proofing techniques, the production process will become more reliable, tolerant, and safe.
The total riskprioritynumber should be calculated by adding all of the riskpriority numbers. This number alone is meaningless because each FMEA has a different number of failure modes and effects. The small RPN is always better than the high RPN. The RPN can be computed for the entire process and/or for the design process only. Once it is calculated, it is easy to determine the areas of greatest concern. There could be less severe failures, but which occur more often and are less detectable. These actions can include specific inspection, testing or quality procedures, redesign (such as selection of new components), adding more redundancy and limiting environmental stresses or operating range. Once the actions have been implemented in the design/process, the new RPN should be checked, to confirm the improvements , , .
ABSTRACT: This paper attempts to analyse the risk involved in the components of an industrial system. Risk is a factor that affects the system performance, operations, cycle time, availability and also the reputation and demand of the product. The study has employed various methods to identify the potential failure causes in the system such as root cause analysis (RCA), riskprioritynumber (RPN) and failure mode effect analysis (FMEA). These methods establish obscure failures which provides a scope for further work. Hence, fuzzy decision making tool technique is applied to interpret the risky component that calls for thorough attention of the sub systems in a process plant
ADES: Adverse Drug Events; AHEQ: Agency for Health Care Research and Quality; ASHP: American Society of Health System Pharmacist; DERS: Dose Error Reduction Soft ware; EPA: Ethiopian Pharmaceutical Association; EPHA: Ethiopian Public Health Association; FDA: Food and Drug Act; MKH: Mettu Karl Hospital; MEA: Failure Mode and Effects Analysis; IOM: institution of medicine; IPF: International Pharmaceutical Federation; IV: Intravenous; JCAHO: Joint Commission Accreditation of health care organization; LASA = Look Alike and Sound Alike; LOS: Length of Hospital Stay; MEPS: Medication Error Prioritization System; MOH: Ministry of Health; NCCMRP: National Coordination Council of Medication errors & Prevention; NGO: None Governmental Organization; NTI= Narrow Therapeutic Index; POC: Point of contact; RPN: Riskprioritynumber; SPSS: Statistical package for Social Sciences; UK: United Kingdom; US: United States; USP: United States pharmacopoeia; WHO: World Health Organization.
Eq.(2.5) deﬁnes the risk of each failure mode as the weighted sum of m risk factors, whereas Eq.(2.6) as the weighted product of m risk factors. For convenience to distinguish between the two risks, we refer to the risk determined by Eq.(2.5) as additive risk and the risk by Eq.(2.6) as multiplicative risk, respectively. It is worthwhile to point out that the deﬁni- tion for additive risks was ﬁrst proposed by Braglia et al. , who deﬁned the RPN as the weighted sum of O, S and D, whereas the deﬁnition for multiplicative risks was ﬁrst proposed by Wang et al , who deﬁned the RPN as the fuzzy weighted geometric mean of the three risk factors O, S, and D, which they referred to as fuzzy riskprioritynumber (FRPN).
In this research risk management is done through undergoing five major steps: 1. Identification of risk factors 2. Assessment of risk factors in terms of its occurrence, consequences and detectability through questionnaire survey. 3. Ranking of risk factors based on RiskPriorityNumber (RPN, function of occurrence, consequence and detectability of risk 4. Risk Allocation 5.Treating the risks by designing risk response strategies.
Traditional FMEA is a listing of potential failures modes, each of which will have at least one (or many) potential effects or consequences of the failure.Decisions on how to improve the quality of productionsare based on RPN (RiskPriorityNumber) which is a very normal method for risk assessment. RPN is evaluated by three components,i.e. the potential causes of the failure (O), the severity of the failure(S)and the detectability of the failure(D). As the cGMP guidance for pharmaceutical plantdirectedin china, these three components are all rated on a scale of 1 to 5.Higher the risk of the failure higher is the value of the RPN.RPN canbe calculated mathematically as RPN=O×D×S. The design and process of traditional FMEA is as figure 1.
In this paper failure mode and effect analysis and fault tree analysis its history , and benefits are discussed and the failure part and the basic reason of failure and the factors of failure are discussed. By the use of FMEA , the failure part ,causes , mode , and their effect are discussed and severity rank and occurrence of failure , detection rank and riskprioritynumber is calculated . And after calculating the riskprioritynumber if find out the number is greater than 100 then it is a serious problem for us so recommended the important actions which should be taken for increasing the life of the part as well as the whole engine and increase the reliability of the component.
Abstract: Today the technological growth is un imaginable. Quality is an essential factor to be considered in every product or service. For the survival of any industry, quality is an essential requirement. Without adequate quality, it is not possible by any industry to compete in the present day market. Centrifugal pumps find a wide application, because of their capabilities to adapt to variable operating conditions, and their ability to discharge different kinds of fluids. Due to these reasons, it is vital to assure quality in the development of centrifugal pumps. One of the important areas of quality assurance is the, "assurance for failure free service". Failure mode and effects analysis (FMEA) is a very powerful and effective methodology for listing all the possible contributing factors of a quality problem. It is a method available for evolving good designs and processes taking inputs from various functions like design, assembly, services etc., It is an essential ingredient of Reliability Engineering. Also it is a mandatory requirement when the companies go for QS 9000. In this project is made to use FMEA for a centrifugal pump. In this investigation, failures in the centrifugal pump components are accounted, i.e., casing, shaft, impeller, bearing and stuffing box. The failure mode,causes, effects and current controls are studied by taking inputs from various pump-manufacturing industries, historical data’s, customers and experts in this field. The rankings, occurrence, severity and detection are calculated to know the riskprioritynumber of the failure mode. For each failure mode, the recommended action is given to reduce the effect of failure.
Mehrzad et.al  were studied about assessment and risk management of potential hazards by Failure Modes and Effect Analysis (FMEA) methos in Yazd Steel Complex. In his work, the risks in different parts of the complex were evaluated by using FMEA method. Jyoti Trivedi et.al  were studied about the FMEA risk management technique for quality control of RMC production. The riskprioritynumber results indicated process failure in terms of irregular grading process, material testing prior use in mixing process which were the important factor to be monitored for quality control. Gunjun joshi et.al  were studied about FMEA and Alternatives v/s Enhanced Risk Assessment Mechanism. In this work, the advantage of using six sigma in Risk Assessment are also pointed out and proposed a novel technique which would overcome the restrictions of existing Risk Management tools.
Virtual projects are created and progress through the drug discovery pipeline by successfully passing a series of go/no-go decision points based on a random number assignment and comparison against the user specified probability of success threshold for the particular mile- stone transition. If the random number is below the threshold, then the project successfully transitions to the next milestone. If not, then the project is terminated and scientists are reassigned to other activities as described below. For example, if a screening project is ready to progress to hit to lead, a random number is generated. If that number is less than the threshold value for moving to the next milestone, then the project is considered to have successfully transitioned to the hit to lead phase. By default, the project is then staffed by one chemist and one biologist. The random number also represents that hit to lead project’s priority, which is then used to calculate the maximum number of additional chemists or biologists that can be added to the project (Equation 2). To illustrate the point, if the target number of hit to lead biologists is 3, the random number is 0.5 and the threshold value is 0.8, then that hit to lead project can only be staffed with a maximum of 2 biologists. In other words, only one additional biologist can be added on top of the original default number of 1. If the random num- ber is zero, then no additional biologists can be added. In this manner, hit to lead projects are resourced com- mensurate with their assigned priority. Hit to lead pro- ject progression to lead optimization proceeds in a similar manner. However, if the transition is successful all attempts are made to fully staff the project as defined by the user. Projects at the lead optimization stage can also receive DMPK support, which effectively acts to ac- celerate SAR decision-making (and consequently com- press the time to next milestone) by allowing analogues with poor in vivo exposure to be identified as early as possible. In this manner cycle times can be reduced.
An ACD call waiting queue holds the calls routed to an ACD group, which have not yet been dealt with by agents. Each queue is administered FIFO (First In-First Out), unless priorities have been defined, in which case calls are queued according to priority. While waiting to be attended by agents, the callers may be connected to ring tone, Music On Hold, or to one or more recorded
Over the past 10 years, NNI funding of ELSI issues has been relatively small compared to the funding allocated for nanotechnology research focused on fundamental phenomena, nanomaterials, nanoscale devices, instrument research, research facilities, and nanomanufacturing. Given the smaller scale of funding, the session participants concluded that it is particularly important for funding agencies and the NNI to think carefully about how to most eff ectively fund ELSI research in this area. Th e session focused on the importance of continuing to fund high- quality social science data collection that allows ELSI researchers to track changes over time, in public risk and benefi t perceptions about nanotechnology, public trust in institutions that regulate nanotechnology, and public knowledge levels about nanotechnology. In addition, the session participants discussed the importance of collecting data that would allow researchers to compare public attitudes about nanotechnology with nanomaterials scientists’ and regulators’ perceptions about nanotechnology to identify areas where there are gaps and commonalities between these groups.
In each case our algorithm minimized context switch, average turnaround time and average waiting time. Even this performs better for without given priority processes (for case 8 shown in Table 11). In every comparison our algorithm gives better performance except a very few comparison such as shown in table 5 for quantum time 5 only (Equal performance to IRR and better than RRWP and RRP), table 7 for quantum time 2 ,5 and table 10 for quantum 100 and 1000(Equal performance to IRR and better than RRWP and RRP) . This can be neglected as it gives equal performances on those comparisons. It also performs better than RRWP in the case of without given priority. Provided all comparisons in above proofs that, proposed algorithm gives better performance than RRP, RRWP, and IRR with accuracy.
In , a Dynamic TDMA protocol for Wireless Sensor Networks (SPARE MAC) has been proposed. In , time is organized in frames and each frame is divided in a signaling sub frame (SSU, consisting of N slots), a wake-up Slot (WS), and a DATA Sub Frame (DSU, consisting of M slots). Each sensor node must choose one of the N slots and will use this to broadcast control packets. The wake-up slot is used to send out tones to notify the neighboring nodes that they have to wake up during the next SSU. The DSU contains slots that are used for receiving data packets. Each sensor has to choose one or more of these and these slots form the Reception schedule of that node. To address the hidden terminal problem, SPARE MAC uses Wake-up Reliable Reservation Aloha protocol, according to which each sensor has to chose one of the control slots in the SSU and uses it for transmitting signaling packets. During SSU, a slot is declared free only if no one is reported using it within 2 hops while in DSU, and a slot is chosen if no other node at one hop has chosen it. To avoid the long delays caused by collisions, a dynamic technique to reserve reception slots is proposed. In this, the transmitters count the consecutive collisions and send a control packet when the threshold is reached. At the receiver, a rate-estimation algorithm is used to allow the receiver to know the number of active sources. Dynamic Bandwidth Adjustment in SPARE MAC makes it possible for any sender to dynamically change its own Reception Schedule by adding, removing, or changing some of its slots.