An integrated RFUCOM – RTOPSIS approach for failure modes and effects analysis: A case of manufacturing industry

2022 ◽  
pp. 108333
Author(s):  
Krantiraditya Dhalmahapatra ◽  
Ashish Garg ◽  
Kritika Singh ◽  
Nirmal Francis Xavier ◽  
J. Maiti
Keyword(s):  
Manufacturing Industry ◽  
Failure Modes

scholarly journals Integration of FMECA and statistical snalysis for predictive maintenance

2021 ◽  
Vol 2 (1) ◽  
pp. 33
Author(s):  
Rabia Ghani
Keyword(s):  
Production Line ◽  
Manufacturing Industry ◽  
Failure Modes ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Time To Failure ◽  
Failure Data ◽  
Criticality Analysis

<p>The estimation of time-to-failure of machines is of utmost importance in the Manufacturing Industry. As the world is moving towards Industry 4.0, it is high time that we progress from the traditional methods, where we wait for a breakdown to occur, to the prognostics based methods. It is the need of the era to be aware of any incident before it occurs. This study provides application of Statistical-based Predictive maintenance. A BOPP Production line has been considered as a case study for this research. Since the inception of the line in 2013, it is evident that 60% of breakdowns are due to lack of maintenance and timely replacement of bearings. Therefore, the research is based on the application of FMECA (Failure Modes, Effects and Criticality Analysis) to determine which bearing in the production line is most prone to failure and determination of which statistical model best fits the failure data of the most critical bearing. The result provides the best distribution fit for the failure data and the fit can be utilized for further study on RUL (Remaining Useful Life) of the bearing through Bayesian Inference.</p>

scholarly journals The application of machine learning to sensor signals for machine tool and process health assessment

2020 ◽  
pp. 095440542096089
Author(s):  
James Moore ◽  
Jon Stammers ◽  
Javier Dominguez-Caballero
Keyword(s):  
Machine Learning ◽  
Machine Tool ◽  
Manufacturing Industry ◽  
Failure Modes ◽  
Test Procedure ◽  
Machining Process ◽  
Sensor Data ◽  
Custom Made ◽  
Production Environments ◽  
Sensor Signals

Due to the latest advancements in monitoring technologies, interest in the possibility of early-detection of quality issues in components has grown considerably in the manufacturing industry. However, implementation of such techniques has been limited outside of the research environment due to the more demanding scenarios posed by production environments. This paper proposes a method of assessing the health of a machining process and the machine tool itself by applying a range of machine learning (ML) techniques to sensor data. The aim of this work is not to provide complete diagnosis of a condition, but to provide a rapid indication that the machine tool or process has changed beyond acceptable limits; making for a more realistic solution for production environments. Prior research by the authors found good visibility of simulated failure modes in a number of machining operations and machine tool fingerprint routines, through the defined sensor suite. The current research set out to utilise this system, and streamline the test procedure to obtain a large dataset to test ML techniques upon. Various supervised and unsupervised ML techniques were implemented using a range of features extracted from the raw sensor signals, principal component analysis and continuous wavelet transform. The latter were classified using convolutional neural networks (CNN); both custom-made networks, and pre-trained networks through transfer learning. The detection and classification accuracies of the simulated failure modes across all classical ML and CNN techniques tested were promising, with all approaching 100% under certain conditions.

Conditional failure profile analysis for multiple failure modes from incomplete two-dimensionalwarranty data

2021 ◽  
pp. 1748006X2110397
Author(s):  
Sanjib Kumar Gupta
Keyword(s):  
Manufacturing Industry ◽  
Failure Modes ◽  
Profile Analysis ◽  
Real Life ◽  
Field Performance ◽  
Time Interval ◽  
Data Set ◽  
Testing Procedures ◽  
Real Life Data ◽  
Intangible Costs

This paper addresses the issue of detecting dominating failure modes of a system from a two-dimensional warranty data set by analyzing conditional failure profile of the system. Two testing procedures have been proposed to test whether any of the failure modes is dominating at a particular time interval and whether there is a change of the failure profile from one time interval to another disjoint time interval, conditioning on a given usage layer. Detecting the problematic failure modes early from the conditional failure profile and taking appropriate actions to reduce the conditional failure probability of the system can significantly reduce both tangible and intangible costs of poor reliability in any manufacturing industry. On the other hand, the study of possible changes of conditional failure profiles has a significant role to assess the field performance of items from one time interval to another time interval for a particular choice of usage layer. The utility of this study is explored with the help of a real-life data set.

Online Degradation Assessment and Adaptive Fault Detection Using Modified Hidden Markov Model

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
Seungchul Lee ◽  
Lin Li ◽  
Jun Ni
Keyword(s):  
Tool Wear ◽  
Markov Model ◽  
Hidden Markov Model ◽  
State Space ◽  
Manufacturing Industry ◽  
Failure Modes ◽  
Hidden Markov ◽  
Statistical Process ◽  
New States ◽  
Novel Method

Online condition monitoring and diagnosis systems play an important role in the modern manufacturing industry. This paper presents a novel method to diagnose the degradation processes of multiple failure modes using a modified hidden Markov model (MHMM) with variable state space. The proposed MHMM is combined with statistical process control to quickly detect the occurrence of an unknown fault. This method allows the state space of a hidden Markov model to be adjusted and updated with the identification of new states. Hence, the online degradation assessment and adaptive fault diagnosis can be simultaneously obtained. Experimental results in a turning process illustrate that the tool wear state can be successfully detected, and previously unknown tool wear processes can be identified at the early stages using the MHMM.

scholarly journals A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method

2021 ◽  
Author(s):  
Melih Yucesan ◽  
Muhammet Gul ◽  
Erkan Celik
Keyword(s):  
Risk Analysis ◽  
Bayesian Network ◽  
Manufacturing Industry ◽  
Failure Modes ◽  
Analysis Tool ◽  
Effect Analysis ◽  
Failure Data ◽  
Subjective Evaluations ◽  
Risk Parameters

AbstractFailure mode and effect analysis (FMEA) is a risk analysis tool widely used in the manufacturing industry. However, traditional FMEA has limitations such as the inability to deal with uncertain failure data including subjective evaluations of experts, the absence of weight values of risk parameters, and not considering the conditionality between failure events. In this paper, we propose a holistic FMEA to overcome these limitations. The proposed approach uses the fuzzy best–worst (FBWM) method in weighting three risk parameters of FMEA, which are severity (S), occurrence (O), and detection (D), and to find the preference values of the failure modes according to parameters S and D. On the other side, it uses the fuzzy Bayesian network (FBN) to determine occurrence probabilities of the failure modes. Experts use a procedure using linguistic variables whose corresponding values are expressed in trapezoidal fuzzy numbers, and determine the preference values of the failure modes according to parameter O in the constructed BN. Thus, the FBN including expert judgments and fuzzy set theory addresses uncertainty in failure data and includes a robust probabilistic risk analysis logic to capture the dependence between failure events. As a demonstration of the approach, a case study was conducted in an industrial kitchen equipment manufacturing facility. The results of the approach have also been compared with existed methods demonstrating its robustness.

Justification of TPM pillars for enhancing the performance of manufacturing industry of Northern India

2019 ◽  
Vol 69 (1) ◽  
pp. 109-133
Author(s):  
Jagdeep Singh ◽  
Harwinder Singh
Keyword(s):  
Manufacturing Industry ◽  
Failure Modes ◽  
Manufacturing Plant ◽  
Content Type ◽  
Manufacturing Enterprises ◽  
Performance Improvements ◽  
Northern India ◽  
Manufacturing Performance ◽  
Critical Components ◽  
Systematic Identification

Purpose The purpose of this paper is to assess TPM pillars for manufacturing performance improvement in the manufacturing organizations of Northern India and to identify critical and non-critical components based on failure history, to minimize machine downtime, maximize component/machine availability and to identify failure modes, their causes and effects of these failures on machines or components in the case company under study. Design/methodology/approach In this paper, TPM pillars in the paint manufacturing plant have been elaborated to ascertain the tangible and intangible benefits accrued as a result of successful TPM implementation. The approach has been directed toward justification of TPM implementation for its support to competitive manufacturing in the context of Indian manufacturing industries. Findings Findings suggest that maintenance planning is more effective than small improvements for achieving benefits from TPM pillars. Moreover, results indicated that critical components show average reliability and failure probability of about 50 percent. Originality/value The present study encompasses systematic identification of maintenance-related losses, setting up of targets regarding maintenance performance improvements and developing guidelines for achieving enhanced manufacturing system performance through strategic TPM implementation in the manufacturing plant, which can also be important to all concerned with maintenance in various manufacturing enterprises.

Adaptive Anomaly Detection Using a Hidden Markov Model

2010 ◽  
Author(s):  
Seungchul Lee ◽  
Lin Li ◽  
Jun Ni
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Manufacturing Industry ◽  
Failure Modes ◽  
Hidden Markov ◽  
Turning Process ◽  
Statistical Process ◽  
Monitoring And Diagnosis ◽  
New States ◽  
Modern Manufacturing

Online condition monitoring and diagnosis systems are very important in the modern manufacturing industry. We present a new method to assess the degradation processes of multiple failure modes using the Hidden Markov Model (HMM). The HMM is combined with statistical process control (SPC) to detect the occurrence of unknown faults. This method allows an HMM to adjust and update the state space with the identification of new states. Hence, the online degradation assessment and adaptive fault diagnosis can be simultaneously obtained. The turning process are used to illustrate that previously unknown tool wear processes can be successfully detected at the early stages using the HMM.

Experiment Study on Machinability of Six Kinds of Wrought Nickel-Based Superalloys

2006 ◽  
Vol 532-533 ◽  
pp. 444-447 ◽  
Author(s):  
Gang Liu ◽  
Ming Chen
Keyword(s):  
Tool Wear ◽  
Wear Mechanism ◽  
Manufacturing Industry ◽  
Failure Modes ◽  
Major Elements ◽  
Practical Significance ◽  
Nickel Based Superalloy ◽  
High Efficient ◽  
Tool Wear Mechanism ◽  
Cutting Vibration

The wrought nickel-based superalloy has been the indispensable material for aviation manufacturing industry, but it is also one of extremely difficult-to-cut materials. Now many researches were focused on the machinability of wrought nickel-based superalloy, and many useful and favorable results can be collected. But most of these researches studied on single kind of wrought nickel-based superalloy, the general and integrated study is absent. In this paper, six typical wrought nickel-based superalloys (GH80A, GH738, GH3030, GH3044, GH4033 and GH4169) were studied. By means of studies on tool wear rate, cutting forces, cutting vibration and tool wear mechanism, the comprehensive comparison of the machinability of wrought nickel-based superalloys was showed. The influences of major elements on the machinability were investigated. The machinability of the six kinds of wrought nickel-based superalloys queues from easiness to difficulty as follows: GH3030, GH3044, GH4033 and GH80A, GH4169, GH738. Finally the comprehensive comparisons of tool failure modes and wear mechanism of these wrought nickel-based superalloys were also presented. Experiment results are comprehensive and have great practical significance to the high efficient machining of wrought superalloys.

Scanning Electron Microscopy in Hybrid Microelectronics

1976 ◽  
Vol 34 ◽  
pp. 456-457
Author(s):  
S. Khadpe ◽  
R. Faryniak
Keyword(s):  
Integrated Circuits ◽  
Thick Film ◽  
Integrated Circuit ◽  
Failure Modes ◽  
Three Dimensional ◽  
Circuit Components ◽  
Hybrid Microcircuit ◽  
Electrical Connections ◽  
Scanning Electron

The Scanning Electron Microscope (SEM) is an important tool in Thick Film Hybrid Microcircuits Manufacturing because of its large depth of focus and three dimensional capability. This paper discusses some of the important areas in which the SEM is used to monitor process control and component failure modes during the various stages of manufacture of a typical hybrid microcircuit.Figure 1 shows a thick film hybrid microcircuit used in a Motorola Paging Receiver. The circuit consists of thick film resistors and conductors screened and fired on a ceramic (aluminum oxide) substrate. Two integrated circuit dice are bonded to the conductors by means of conductive epoxy and electrical connections from each integrated circuit to the substrate are made by ultrasonically bonding 1 mil aluminum wires from the die pads to appropriate conductor pads on the substrate. In addition to the integrated circuits and the resistors, the circuit includes seven chip capacitors soldered onto the substrate. Some of the important considerations involved in the selection and reliability aspects of the hybrid circuit components are: (a) the quality of the substrate; (b) the surface structure of the thick film conductors; (c) the metallization characteristics of the integrated circuit; and (d) the quality of the wire bond interconnections.

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