A Review on Vibration Monitoring Techniques for Predictive Maintenance of Rotating Machinery

Romanssini, Marcelo; Aguirre, Paulo César C. de; Severo, Lucas Compassi; Girardi, Alessandro

doi:10.3390/eng4030102

Cited by 23 publications

(11 citation statements)

References 102 publications

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“…Data collected by sensors which record oscillations or motions within a structure or system is known as vibration data. It collects information that is important in evaluating the condition [32] and functionality of machinery or systems, such as vibration patterns, amplitude, and frequency. Vibration sensors monitor the intensity and frequency of vibrations in industrial components with unusual patterns indicating possible mechanical problems such as imbalances [9,33], wear [9,25,27,[34][35][36][37] or misalignment [9,[38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

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An In-Depth Study of Vibration Sensors for Condition Monitoring

Hassan,

Panduru,

Walsh

2024

Sensors

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Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. Predictive maintenance is a maintenance strategy that predicts possible breakdowns of equipment using data analysis, pattern recognition, and machine learning. In this paper, vibration-based condition monitoring studies are reviewed with a focus on the devices and methods used for data collection. For measuring vibrations, different accelerometers and their technologies were investigated and evaluated within data collection contexts. The studies collected information from a wide range of sources in the heavy machinery. Throughout our review, we came across some studies using simulations or existing datasets. We concluded in this review that due to the complexity of the situation, we need to use more advanced accelerometers that can measure vibration.

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Section: Introductionmentioning

confidence: 99%

“…Several methods are used to process vibration data obtained from heavy machinery for doing condition monitoring [32]. Some methods use the Fast Fourier Transform (FFT) analysis to highlight frequencies associated with anomalies or problems in the machinery by converting vibration signals from the time domain to the frequency domain [46].…”

Section: Introductionmentioning

confidence: 99%

An In-Depth Study of Vibration Sensors for Condition Monitoring

Hassan,

Panduru,

Walsh

2024

Sensors

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“…Furthermore, it has also been reported [20] that NP strain sensors have the potential to detect acoustic vibrations with an infinitesimal amplitude, which extends the application field of NP arrays towards precision devices with ultrafast dynamics. This possibility opens the way to exploit NP strain sensors as vibration sensors, which are of interest in various application fields ranging from voice recognition [21] to condition-based monitoring (CbM) of machines, which can enable cost savings on machine life and maintenance [22]. A recent review on the use of strain sensors for vibration sensing suggested that resistive strain sensors have a potential that needs to be further investigated, due mainly to Furthermore, it has also been reported [20] that NP strain sensors have the potential to detect acoustic vibrations with an infinitesimal amplitude, which extends the application field of NP arrays towards precision devices with ultrafast dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…A recent review on the use of strain sensors for vibration sensing suggested that resistive strain sensors have a potential that needs to be further investigated, due mainly to Furthermore, it has also been reported [20] that NP strain sensors have the potential to detect acoustic vibrations with an infinitesimal amplitude, which extends the application field of NP arrays towards precision devices with ultrafast dynamics. This possibility opens the way to exploit NP strain sensors as vibration sensors, which are of interest in various application fields ranging from voice recognition [21] to condition-based monitoring (CbM) of machines, which can enable cost savings on machine life and maintenance [22]. A recent review on the use of strain sensors for vibration sensing suggested that resistive strain sensors have a potential that needs to be further investigated, due mainly to their low cost and low weight, especially when compared to accelerometers, which are currently the industry standard for vibration sensing and monitoring [23].…”

Section: Introductionmentioning

confidence: 99%

Vibration Sensors on Flexible Substrates Based on Nanoparticle Films Grown by Physical Vapor Deposition

Aslanidis,

Sarigiannidis,

Skotadis

et al. 2024

Materials

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Flexible electronics have gained a lot of attention in recent years due to their compatibility with soft robotics, artificial arms, and many other applications. Meanwhile, the detection of acoustic frequencies is a very useful tool for applications ranging from voice recognition to machine condition monitoring. In this work, the dynamic response of Pt nanoparticles (Pt NPs)-based strain sensors on flexible substrates is investigated. the nanoparticles were grown in a vacuum by magnetron-sputtering inert-gas condensation. Nanoparticle sensors made on cracked alumina deposited by atomic layer deposition on the flexible substrate and reference nanoparticle sensors, without the alumina layer, were first characterized by their response to strain. The sensors were then characterized by their dynamic response to acoustic frequency vibrations between 20 Hz and 6250 Hz. The results show that alumina sensors outperformed the reference sensors in terms of voltage amplitude. Sensors on the alumina layer could accurately detect frequencies up to 6250 Hz, compared with the reference sensors, which were sensitive to frequencies up to 4250 Hz, while they could distinguish between two neighboring frequencies with a difference of no more than 2 Hz.

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“…In [4], a comprehensive review is presented on signal processing techniques for vibration-based feature extraction in structural health monitoring (SHM). The work in [5] addresses the application of vibration-based condition monitoring techniques for the PdM of rotating Machinery. It provides a comprehensive review of vibration data acquisition and analysis, as well as the methods used for fault interpretation and diagnosis, including data acquisition, data transmission, signal processing, and fault detection.…”

Section: Introductionmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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A Review on Vibration Monitoring Techniques for Predictive Maintenance of Rotating Machinery

Cited by 23 publications

References 102 publications

An In-Depth Study of Vibration Sensors for Condition Monitoring

An In-Depth Study of Vibration Sensors for Condition Monitoring

Vibration Sensors on Flexible Substrates Based on Nanoparticle Films Grown by Physical Vapor Deposition

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

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