fmea safety valve in stock

RETROFIT KIT FOR FMEA 1/2 INCH Includes valve with long buttonWe were the first to market these kits when FMEA valves were discontinued.Replaces most 1/2 inch FMEA valvesIncludes• Adapter to convert pilot from 1/4 inch to 3/16 • one 1/2 TS11 VALVE• one brass extension • one 60" thermocouple• Fiberglass shield for thermocoupleThese kits will install in most situations without any additional hardware. In some cases it might be necessary to slightly enlarge hole on cover where button protrudes.

• Inlet outlet 1/2" NPT•Valve provides positive shut off of gas whenpilot is out •1/4" O.D. tubing outlet only for pilot• For Blodgett convection ovens models BCG, FA/GZL• For Blodgett deck pizza ovens models 900 series(after 3/61), 999, 1000, 1048, 1060• Blodgett nos. 4492, 11523

Filo, G., Fabis-Domagala, J., Domagala, M., Lisowski, E., Momeni, H., 2018. The idea of fuzzy logic usage in a sheet-based FMEA analysis of mechanical systems. MATEC Web Conf., 183, art. 03009. DOI: 10.1051/matecconf/20181830300910.1051/matecconf/201818303009

Domagala, M., Momeni, H., Domagala-Fabis, J., Filo, G., Krawczyk, M., 2018a. Simulation of Cavitation Erosion in a Hydraulic Valve. Materials Research Proceedings, 5, 1-6. DOI: 10.21741/9781945291814-110.21741/9781945291814-1

Domagala, M., Momeni, H., Domagala-Fabis, J., Filo, G., Kwiatkowski, D., 2018b. Simulation of Particle Erosion in a Hydraulic Valve. Materials Research Proceedings, 5, 17-24. DOI: 10.21741/9781945291814-410.21741/9781945291814-4

Karpisz, D., Kielbus, A., 2019. The Revitalization of Radar System as a Case of Functional and Information Security Problems. System Safety: Human - Technical Facility – Environment, 1, 692-699. DOI: 10.2478/czoto-2019-008810.2478/czoto-2019-0088

Kielbus, A., Karpisz, D., 2019. Risk management as a process security tool. System Safety: Human-Technical Facility-Environment, 1, 234-239. DOI: 10.2478/czoto-2019-003010.2478/czoto-2019-0030

1. The FMDA safety valve is the only type with the thermocouple permanently attached to it.  This means the thermocouple cannot be replaced; the entire safety valve must be replaced if the thermocouple fails.  The easiest way to identify an FMDA type safety is a ½” diameter red button on the bottom of the valve.  You must know the gas pipe size and if the pilot tube is an “in and out” or an “out only.”  An “in and out” safety valve has two threaded holes at the top of the part, one for gas for the pilot to come in and one for gas to go out.  An “out only” safety valve has just one threaded hole to connect gas for the pilot to.

2. The BASO safety valve can vary in design depending on the piece of equipment it is on, so it is important to know the brand name, model and serial number of the piece of equipment to get the correct safety valve the first time.  The easiest way to identify a BASO valve is by the 15/16” diameter red pilot button.  The thermocouple is separate from the safety.

3. The TS type safety valve is the only one that can be rebuilt.  It is similar to the FMDA and BASO types in that it has “in and out” or “out only” pilot tubing, so you must know what is in your equipment.  A rebuilt kit is available in both and it is not necessary to replace the body unless it is damaged.  The body has no moving parts in it.  The easiest way to identify the TS safety is by the 5/8” diameter red button.  The thermocouple is also separate from this safety, similar to the BASO.

If the wire leads are screwed to the top terminal block, and two tubes are coming out of the top of the valve, it is the tubing type combination safety valve.

Failure Modes and Effects Analysis (FMEA) is a systematic, proactive method for evaluating a process to identify where and how it might fail and to assess the relative impact of different failures, in order to identify the parts of the process that are most in need of change. FMEA includes review of the following:

Teams use FMEA to evaluate processes for possible failures and to prevent them by correcting the processes proactively rather than reacting to adverse events after failures have occurred. This emphasis on prevention may reduce risk of harm to both patients and staff. FMEA is particularly useful in evaluating a new process prior to implementation and in assessing the impact of a proposed change to an existing process.

As a kind of reliability analysis and risk management technique, failure mode and effect analysis (FMEA) has been widely used in rail traffic risk analysis [1,2]. In practice, the risk prioritization of every failure mode can be obtained by a risk priority number (RPN) through three risk factors of occurrence (O), severity (S) and detection (D). Then, the key components can be identified by the fusion of the RPN of each failure mode [3].

The most important and urgent business is to determine the criticality of the system component in the operation of railway train. Later, the maintenance optimization and improvement can be put into practice for the safety and reliability operation.

As stated above, there is a pressing need to further improve the conventional FMEA approach for the railway train risk analysis and prioritization. In order to overcome the shortcomings of traditional FMEA approach, some applications employed the risk information judgments with linguistic terms of fuzzy set theory. Triangular fuzzy numbers [8,9], interval fuzzy numbers [10], hesitant fuzzy numbers [11,12] and intuitionistic fuzzy numbers (IFNs) [13,14] have been adopted with MCDM methods to deal with the risk prioritization problems. By comparison, IFNs can be characterized by the membership function (MF) and non-membership function (NMF) to illustrate the positive and negative degree, respectively, which can be more flexible than other fuzzy terms. Nevertheless, it can be a challenge to determine the accurate data of their MFs and NMFs. Hence, type-2 IFNs have been put forward to handle the fuzzy problems in a way. Type-2 IFNs possess many advantages over IFNs, as their MFs and NMFs are themselves fuzzy, making it possible to model and minimize the effects of indeterminacy in fuzzy matters. Yu, Wang and Wang [15] extended IFNs with interval numbers as an application of type-2 IFNs for the uncertainty in the site-selection problem. Wei et al. [16] applied interval valued IFNs with entropy measures to overcome the existing entropy matters. However, the terminal point of MFs and NMFs can be difficult to determine and further enlarge the interval area after arithmetic operation. Therefore, Liu and Yuan [17] proposed TFNIFNs as another application of type-2 IFS to describe uncertainty and fuzziness. Compared with interval valued IFNs, TFNIFNs [18] can be better utilized in the fields of fuzziness as the triangular fuzzy number between 0 and 1, which can make TFNIFNs more flexible and reasonable.

On the other hand, some papers regarded the risk prioritization as a multi-criteria decision making (MCDM) problem [19,20,21,22,23,24]. Consequently, the MCDM model has been widely applied to solve the drawbacks in FMEA. Among the different MCDM technologies, TOSPIS and VIKOR methods are the widely used applications. Liu et al. [25] developed FMEA and VIKOR method to identify the risk of general anesthesia process. Lo et al. [26] proposed a novel FMEA model based on TOPSIS method for the equipment product risk identification. Furthermore, Mandal et al. [27] present a FMEA framework with fuzzy VIKOR approach for safety critical resources identification and risk mitigation purposes. Li et al. [28] tried to identify, evaluate and eliminate potential failures of the spindle box system by an advanced FMEA combined with fuzzy TOPSIS. According to the above researches, the solution obtained by the VIKOR method [29] is an aggregation of all the criteria, the relative importance of the criteria, and a balance between total and individual satisfaction. However, the solution determined by TOPSIS method considers the distances from the ideal point and negative-ideal point without considering their relative importance. Therefore, the comparison cases indicate that the VIKOR method can be slightly better than the TOPSIS method [30,31].

Although, to some extent, these efforts have eliminated the shortcomings of the conventional FMEA, there is a crucial issue that has not been fully coped with, namely, experts’ risk sensitiveness and decision-making psychological behavior. In order to solve this problem, prospect theory [32,33,34,35] combined with MCDM methods can be used to conduct risk prioritization in FMEA model by the consideration of experts’ risk sensitiveness and decision-making psychological behavior. However, traditional prospect theory also has the unacceptable drawback about violations of dominance. Consequently, cumulative prospect theory [36] has been proposed to handle this matter. In cumulative prospect theory, the probability weight is replaced by cumulative probability weight and makes it a clear logic as well as a relatively simple computation procedure, thus, cumulative prospect theory can be extensively applied in various MCDM problems [37,38,39,40].

According to the discussion above, in this paper, we develop an extended FMEA model based on cumulative prospect theory and type-2 intuitionistic fuzzy VIKOR for the railway train risk prioritization. In order to handle such situations where experts with different risk sensitiveness and decision-making psychological behavior towards different failure modes of railway train, cumulative prospect theory combined with TFNIFNs is adopted to depict the different risk sensitiveness and psychological behavior of experts. In addition, the VIKOR approach associated with entropy weight method is also carried out to fuse the risk information under different risk factors. Therefore, the final risk prioritization order can be obtained based on the compromise results of VIKOR. At last, a case study of railway train bogie system is utilized to illustrate the proposed extended FMEA model.

In the light of the above analysis, the contributions of this paper can be summarized as follows:The proposed risk component prioritization model based on FMEA framework considers all possible failure modes of railway train without losing any valid state information.

The extended FMEA model combined with cumulative prospect theory considers the experts’ risk sensitiveness and decision-making psychological behavior which can obtain a relatively objective and reasonable risk prioritization outcome.

The rest of this paper can be organized as follows. In Section 2, the basic theories related to type-2 intuitionistic fuzzy numbers, VIKOR and cumulative prospect theory are briefly introduced. Section 3 presents the extended FMEA model based on cumulative prospect theory and type-2 intuitionistic fuzzy VIKOR for the railway train risk prioritization. In Section 4, a case study of the railway train bogie system is selected to illustrate the application and effectiveness of the proposed method. In Section 5, the conclusions and future research directions of this study are provided.