Research

22 pages, 2510 KiB

Open AccessArticle

New FMEA Risks Ranking Approach Utilizing Four Fuzzy Logic Systems

by Jelena Ivančan and Dragutin Lisjak

Machines 2021, 9(11), 292; https://doi.org/10.3390/machines9110292 - 16 Nov 2021

Cited by 22 | Viewed by 3162

Process equipment and plant maintenance problems are complex in the oil refinery business, since effective maintenance needs to ensure the reliability and availability of the plant. Failure Mode and Effects Analysis (FMEA) is a risk assessment tool that aims to determine possible failure [...] Read more.

Process equipment and plant maintenance problems are complex in the oil refinery business, since effective maintenance needs to ensure the reliability and availability of the plant. Failure Mode and Effects Analysis (FMEA) is a risk assessment tool that aims to determine possible failure modes, and to reduce the ratio of unknown failure modes, by identifying business-critical systems and the risks of their failures. For the identified failure modes, FMEA determines risk mitigation action(s). The goal is to prevent failure and keep assets and plants running at peak performance by providing fully integrated operations, maintenance, turnarounds, modifications, and asset integrity solutions, during all phases of the asset life cycle. This research was based on FMEA use/application in refineries’ units, and proposes the new fuzzy FMEA risk quantification approach method: “four fuzzy logic system”. The model included a pre-assessment, by sets of fuzzy logic systems, that examined the input parameters that affected the variables of severity, occurrence, and detectability. The proposed model prioritized risks better and addressed the drawbacks of the conventional FMEA method. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis, and Recovery: Concept, Modeling, and Optimization)

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18 pages, 4921 KiB

Open AccessArticle

Development of a Low-Cost Vibration Measurement System for Industrial Applications

by Adrian Villarroel, Grover Zurita and Romeo Velarde

Machines 2019, 7(1), 12; https://doi.org/10.3390/machines7010012 - 01 Feb 2019

Cited by 10 | Viewed by 6827

Abstract

Vibration-Based Condition Monitoring (VBCM) provides essential data to perform Condition-Based Maintenance for efficient, optimal, reliable, and safe industrial machinery operation. However, equipment required to perform VBCM is often relatively expensive. In this paper, a low-cost vibration measurement system based on a microcontroller platform [...] Read more.

Vibration-Based Condition Monitoring (VBCM) provides essential data to perform Condition-Based Maintenance for efficient, optimal, reliable, and safe industrial machinery operation. However, equipment required to perform VBCM is often relatively expensive. In this paper, a low-cost vibration measurement system based on a microcontroller platform is presented. The FRDM K64F development board was selected as the most suitable for fulfilling the system requirements. The industrial environment is highly contaminated by noise (electromagnetic, combustion, airborne, sound borne, and mechanical noise). Developing a proper antialiasing filter to reduce industrial noise is a real challenge. In order to validate the developed system, evaluations of frequency response and phase noise were carried out. Additionally, vibration measurements were recorded in the industry under different running conditions and machine configurations. Data were collected simultaneously using a standard reference system and the low-cost vibration measurement system. Results were processed using Fast Fourier Transform and Welch’s method. Finally, a low-cost vibration measurement system was successfully created. The validation process demonstrates the robustness, reliability, and accuracy of this research approach. Results confirm a correlation between signal frequency spectrum obtained using both measurement systems. We also introduce new guidelines for practical data storage, communications, and validation process for vibration measurements. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis, and Recovery: Concept, Modeling, and Optimization)

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16 pages, 4281 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

by Kenisuomo C. Luwei, Akilu Yunusa-Kaltungo and Yusuf A. Sha’aban

Machines 2018, 6(4), 59; https://doi.org/10.3390/machines6040059 - 20 Nov 2018

Cited by 45 | Viewed by 5185

Abstract

The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount [...] Read more.

The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis, and Recovery: Concept, Modeling, and Optimization)

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17 pages, 3377 KiB

Open AccessArticle

A Reliability-Centered Maintenance Study for an Individual Section-Forming Machine

by Samuel Okwuobi, Felix Ishola, Oluseyi Ajayi, Enesi Salawu, Abraham Aworinde, Obafemi Olatunji and Stephen A. Akinlabi

Machines 2018, 6(4), 50; https://doi.org/10.3390/machines6040050 - 26 Oct 2018

Cited by 16 | Viewed by 9745

Abstract

This study investigated the breakdown trend in an automated production with an aim to recommend the application of reliability-centered maintenance (RCM) for improved productivity via a new preventive maintenance (PM) program. An individual section-forming machine (ISM)—a glass blowing machine for making glass bottles—was [...] Read more.

This study investigated the breakdown trend in an automated production with an aim to recommend the application of reliability-centered maintenance (RCM) for improved productivity via a new preventive maintenance (PM) program. An individual section-forming machine (ISM)—a glass blowing machine for making glass bottles—was used as the case study for an automated production system. The machine parts and the working mechanisms were analysed with a special focus on methods of processes and procedures. This will enable the ISM maintenance department to run more effectively and achieve its essential goal of ensuring effective machine operation and reduction in machine downtime. In this work, information is provided on the steps and procedures to identify critical components of the ISM using failure modes and effect analysis (FMEA) as a tool to come up with an optimal and efficient maintenance program using the reliability data of the equipment’s functional components. A relationship between the failure rate of the machine components and the maintenance costs was established such that using the recommended PM program demonstrates evidence of an improvement in the machine’s availability, safety, and cost-effectiveness and will result in an increase in the company’s profit margin. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis, and Recovery: Concept, Modeling, and Optimization)

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13 pages, 1789 KiB

Open AccessFeature PaperArticle

Experimental Evidence of the Speed Variation Effect on SVM Accuracy for Diagnostics of Ball Bearings

by Jacopo Cavalaglio Camargo Molano, Riccardo Rubini and Marco Cocconcelli

Machines 2018, 6(4), 48; https://doi.org/10.3390/machines6040048 - 18 Oct 2018

Cited by 1 | Viewed by 3264

Abstract

In recent years, we have witnessed a considerable increase in scientific papers concerning the condition monitoring of mechanical components by means of machine learning. These techniques are oriented towards the diagnostics of mechanical components. In the same years, the interest of the scientific [...] Read more.

In recent years, we have witnessed a considerable increase in scientific papers concerning the condition monitoring of mechanical components by means of machine learning. These techniques are oriented towards the diagnostics of mechanical components. In the same years, the interest of the scientific community in machine diagnostics has moved to the condition monitoring of machinery in non-stationary conditions (i.e., machines working with variable speed profiles or variable loads). Non-stationarity implies more complex signal processing techniques, and a natural consequence is the use of machine learning techniques for data analysis in non-stationary applications. Several papers have studied the machine learning system, but they focus on specific machine learning systems and the selection of the best input array. No paper has considered the dynamics of the system, that is, the influence of how much the speed profile changes during the training and testing steps of a machine learning technique. The aim of this paper is to show the importance of considering the dynamic conditions, taking the condition monitoring of ball bearings in variable speed applications as an example. A commercial support vector machine tool is used, tuning it in constant speed applications and testing it in variable speed conditions. The results show critical issues of machine learning techniques in non-stationary conditions. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis, and Recovery: Concept, Modeling, and Optimization)

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40 pages, 46795 KiB

Open AccessArticle

Gas Path Fault and Degradation Modelling in Twin-Shaft Gas Turbines

by Samuel Cruz-Manzo, Vili Panov and Yu Zhang

Machines 2018, 6(4), 43; https://doi.org/10.3390/machines6040043 - 01 Oct 2018

Cited by 17 | Viewed by 6462

Abstract

In this study, an assessment of degradation and failure modes in the gas-path components of twin-shaft industrial gas turbines (IGTs) has been carried out through a model-based analysis. Measurements from twin-shaft IGTs operated in the field and denoting reduction in engine performance attributed [...] Read more.

In this study, an assessment of degradation and failure modes in the gas-path components of twin-shaft industrial gas turbines (IGTs) has been carried out through a model-based analysis. Measurements from twin-shaft IGTs operated in the field and denoting reduction in engine performance attributed to compressor fouling conditions, hot-end blade turbine damage, and failure in the variable stator guide vane (VSGV) mechanism of the compressor have been considered for the analysis. The measurements were compared with simulated data from a thermodynamic model constructed in a Simulink environment, which predicts the physical parameters (pressure and temperature) across the different stations of the IGT. The model predicts engine health parameters, e.g., component efficiencies and flow capacities, which are not available in the engine field data. The results show that it is possible to simulate the change in physical parameters across the IGT during degradation and failure in the components by varying component efficiencies and flow capacities during IGT simulation. The results also demonstrate that the model can predict the measured field data attributed to failure in the gas-path components of twin-shaft IGTs. The estimated health parameters during degradation or failure in the gas-path components can assist the development of health-index prognostic methods for operational engine performance prediction. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis, and Recovery: Concept, Modeling, and Optimization)

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18 pages, 2805 KiB

Open AccessFeature PaperArticle

Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components

by Francesco Cannarile, Michele Compare, Piero Baraldi, Francesco Di Maio and Enrico Zio

Machines 2018, 6(3), 34; https://doi.org/10.3390/machines6030034 - 01 Aug 2018

Cited by 12 | Viewed by 4037

Abstract

This work presents a method to improve the diagnostic performance of empirical classification system (ECS), which is used to estimate the degradation state of components based on measured signals. The ECS is embedded in a homogenous continuous-time, finite-state semi-Markov model (HCTFSSMM), which adjusts [...] Read more.

This work presents a method to improve the diagnostic performance of empirical classification system (ECS), which is used to estimate the degradation state of components based on measured signals. The ECS is embedded in a homogenous continuous-time, finite-state semi-Markov model (HCTFSSMM), which adjusts diagnoses based on the past history of components. The combination gives rise to a homogeneous continuous-time finite-state hidden semi-Markov model (HCTFSHSMM). In an application involving the degradation of bearings in automotive machines, the proposed method is shown to be superior in classification performance compared to the single-stage ECS. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis, and Recovery: Concept, Modeling, and Optimization)

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20 pages, 2659 KiB

Open AccessArticle

Non-Destructive Testing for Winding Insulation Diagnosis Using Inter-Turn Transient Voltage Signature Analysis

by Nadia Radja, M’hemed Rachek and Soraya Nait Larbi

Machines 2018, 6(2), 21; https://doi.org/10.3390/machines6020021 - 10 May 2018

Cited by 3 | Viewed by 4601

Abstract

The paper proposes a novel approach to assess the integrity of Electrical Insulation Systems (EIS) by evaluating the response of the Transient Voltage Signature Analysis (VSA) to voltage source inverters correlated with changes in the Insulation Capacitance (IC). The involved model structures are [...] Read more.

The paper proposes a novel approach to assess the integrity of Electrical Insulation Systems (EIS) by evaluating the response of the Transient Voltage Signature Analysis (VSA) to voltage source inverters correlated with changes in the Insulation Capacitance (IC). The involved model structures are derived from the in-situ estimation of high-frequency electromagnetic RLMC lumped network parameters. Different physical phenomena such as inductive and capacitive effects, as well as skin and proximity effects are combined. To account for these phenomena, we use an approach based on equivalent multi-transmission line electric circuits with distributed parameters (R: resistances, L, M: self and mutual inductances, and C: capacitances) which are frequency-dependent. Using the finite element method, firstly the turn-to-ground and turn-to-turn capacitance parameters are performed by solving an electrostatic model with a floating electric potential approach, and secondly, the resistance and self/mutual inductances are computed from the strongly coupled magneto-harmonic and total current density equations, including the conduction and displacement eddy current densities. The sensitivity of the capacitances is measured according to insulation thickness, and the dielectric properties are adopted to test the degradation order scenarios of the EIS and comparing their time and frequency domains of transient voltage waveform behavior with respect to healthy assessed insulation systems. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis, and Recovery: Concept, Modeling, and Optimization)

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