Statistical quality control through process self-induced vibration spectrum analysis

López-Escobar, Carlos; González-Palma, R.; Gomar, David Almorza; Mayorga, P.; Carnero, María Carmen

doi:10.1007/s00170-011-3462-8

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…The benefits an organization can obtain from the use of vibration analysis include improvements in availability [1], quality [2], safety [3], cost [4,5], and the environment. Furthermore, its versatility with regard to the machines it can be applied to and the typology of faults or anomalies that can be detected [6], along with the possibility of developing an automatic diagnostic system [7], have made it the most widely used predictive method [8].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Multicriteria Model for Selection of Vibration Technology

Carnero

2016

Shock and Vibration

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The benefits of applying the vibration analysis program are well known and have been so for decades. A large number of contributions have been produced discussing new diagnostic, signal treatment, technical parameter analysis, and prognosis techniques. However, to obtain the expected benefits from a vibration analysis program, it is necessary to choose the instrumentation which guarantees the best results. Despite its importance, in the literature, there are no models to assist in taking this decision. This research describes an objective model using Fuzzy Analytic Hierarchy Process (FAHP) to make a choice of the most suitable technology among portable vibration analysers. The aim is to create an easy-to-use model for processing, manufacturing, services, and research organizations, to guarantee adequate decision-making in the choice of vibration analysis technology. The model described recognises that judgements are often based on ambiguous, imprecise, or inadequate information that cannot provide precise values. The model incorporates judgements from several decision-makers who are experts in the field of vibration analysis, maintenance, and electronic devices. The model has been applied to a Health Care Organization.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Multicriteria Model for Selection of Vibration Technology

Carnero

2016

Shock and Vibration

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“…A higher level of computer intelligence could be implemented in these systems with the use of the neural networks [10,11]. By combining the statistical quality control, the vibration analysis [12] can result in a highlyautomated and controlled manufacturing process in the future. Vibration-based measurement systems became very common in industrial environment nowadays.…”

Section: Introductionmentioning

confidence: 99%

Managing measured vibration data for malfunction detection of an assembled mechanical coupling

Manhertz

Gardonyi

Por

2014

Int J Adv Manuf Technol

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Vibration data recorded by accelerometer fixed on a mechanical coupling were treated using different data processing techniques as estimating general root mean square, partial root means square, mean, variance, partial variance, kurtosis, full Fourier spectra, and partial spectra, as well as using sequential probability ratio test (SPRT). The numerical results of these methods measured on an electromotive drive train-coupling were collected into a common data set, and a search method of deviation from the normal behavior was elaborated. This selects the bad couplings if at least three of the presented qualification parameters crosses either theoretically or experimentally settled limits. It was shown that the confidence level of such classification of good and bad species was better than 95 % compared with the results of thoroughly tested ones by conventional methods.

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“…An extensive review on suitable data labeling techniques may be found in Jahnke (2015). Features analysis for industrial data sets may be found in Lopez-Escobar et al (2012), Amarnath and Krishna (2014) and Yan et al (2014), along with a description of the most commonly used features generated from temporal, spectral and vibration signals. The choice of the most appropriate model of incoming failure is the core activity in CBM.…”

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confidence: 99%

Condition-based maintenance: an industrial application on rotary machines

Acernese

Vecchio

Tipaldi

et al. 2020

JQME

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PurposeThe purpose of this paper is to describe a model for the design and development of a condition-based maintenance (CBM) strategy for the cutting group of a labeling machine. The CBM aims to ensure the quality of labels' cut and overall machine performances.Design/methodology/approachIn developing a complete CBM strategy, two main difficulties have to be overcome: (1) appropriately dealing with incomplete and low-quality production database and (2) selecting the most promising predictive model. The first issue has been addressed applying data cleansing operations and creating ad hoc methodology to enlarge the training data. The second issue has been handled developing and comparing an empirical model with a machine learning (ML)-based model; the comparison has been performed assessing capabilities thereof in predicting erroneous label cuts on data obtained from an operating plant located in Italy.FindingsResearch results showed that both empirical and ML-based approaches exhibit good performances in detecting the operating conditions of the cutting machine. The advantage of adopting an ML-based model is that it can be used not only as a condition indicator (i.e. a model able to continuously provide the health status of an asset) but also in predictive maintenance policies (i.e. a CBM carried out following a forecast of the degradation of the item).Research limitations/implicationsThe study described in this manuscript has been developed on the practices of a labeling machine developed by an international company manufacturing bottling lines for beverage industry. The proposed approach might need some customization in case it is applied to other industries. Future researches can validate the applicability of such models on different rotary machines in other companies and similar industries.Originality/valueThe main contribution of this paper lies in the empirical demonstration of the benefits of CBM and predictive maintenance in manufacturing, through the overcoming of a specific production issue. The large number of variables involved in thin label cutting lines (film thickness between 30 and 38 µm), the high throughput and the high costs due to production interruptions render the prediction of non-conforming labels an economically relevant, albeit challenging, goal. Moreover, despite the large scientific literature on CBM in rolling bearing and face cutting movements, papers dealing with rotary labeling machines are very unusual and unique.

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Statistical quality control through process self-induced vibration spectrum analysis

Cited by 8 publications

References 30 publications

Fuzzy Multicriteria Model for Selection of Vibration Technology

Fuzzy Multicriteria Model for Selection of Vibration Technology

Managing measured vibration data for malfunction detection of an assembled mechanical coupling

Condition-based maintenance: an industrial application on rotary machines

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