Test of measurement device for the estimation of leakage flow rate in pneumatic pipeline systems

Dindorf, R.; Woś, P.

doi:10.1177/0020294018808681

Cited by 15 publications

(17 citation statements)

References 13 publications

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“…There are several challenges with using this equipment such as reachability of the leak since hand-held tools are limited in application to very short distances. Dindorf and Wos (2018) developed a new method of indirect flow rate measurement to detect leaks in pneumatic pipeline systems. Using a ML data-driven fault detection for air leaks is very promising, but it has its own challenges and gaps.…”

Section: Fault Detection Signalsmentioning

confidence: 99%

Air leaks fault detection in maintenance using machine learning

Barakat,

Hajeir,

Alattal

et al. 2024

JQME

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PurposeThe objective of this paper is to develop a condition-based maintenance (CBM) scheme for pneumatic cylinders. The CBM scheme will detect two common types of air leaking failure modes and identify the leaky/faulty cylinder. The successful implementation of the proposed scheme will reduce energy consumption, scrap and rework, and time to repair.Design/methodology/approachEffective implementation of maintenance is important to reduce operation cost, improve productivity and enhance quality performance at the same time. Condition-based monitoring is an effective maintenance scheme where maintenance is triggered based on the condition of the equipment monitored either real time or at certain intervals. Pneumatic air systems are commonly used in many industries for packaging, sorting and powering air tools among others. A common failure mode of pneumatic cylinders is air leaks which is difficult to detect for complex systems with many connections. The proposed method consists of monitoring the stroke speed profile of the piston inside the pneumatic cylinder using hall effect sensors. Statistical features are extracted from the speed profiles and used to develop a fault detection machine learning model. The proposed method is demonstrated using a real-life case of tea packaging machines.FindingsBased on the limited data collected, the ensemble machine learning algorithm resulted in 88.4% accuracy. The algorithm can detect failures as soon as they occur based on majority vote rule of three machine learning models.Practical implicationsEarly air leak detection will improve quality of packaged tea bags and provide annual savings due to time to repair and energy waste reduction. The average annual estimated savings due to the implementation of the new CBM method is $229,200 with a payback period of less than two years.Originality/valueTo the best of the authors’ knowledge, this paper is the first in terms of proposing a CBM for pneumatic systems air leaks using piston speed. Majority, if not all, current detection methods rely on expensive equipment such as infrared or ultrasonic sensors. This paper also contributes to the research gap of economic justification of using CBM.

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Section: Fault Detection Signalsmentioning

confidence: 99%

Air leaks fault detection in maintenance using machine learning

Barakat,

Hajeir,

Alattal

et al. 2024

JQME

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“…Temperature must also be considered when the internal pressure rapidly decreases and this solution is best applied in industrial compressed air piping systems. 121 This research analyzed the primary phases of leak detection and localization systems and noted the complex nature of having reliable leak detection tools. The next stage involves developing a real-time monitoring system capable of detecting large and small leaks.…”

Section: Effectiveness Of Ldms and Llms Against Possible Leakagementioning

confidence: 99%

Degradation mechanisms associated with metal pipes and the effective impact of LDMs and LLMs in water transport and distribution

Agala

Khan

Starr

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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The effective operation of water management systems is contingent upon leak localization and detection – a common problem that is more acute in large networks. This paper reviews the salient literature in this context and demonstrates the effectiveness of leakage location methods (LLMs) and leakage detection methods (LDMs). Although there is a significant amount of literature that discusses leakage localization and detection technologies, an academic lacuna still exists concerning the linkage between degradation mechanisms and LDMs and do not cover or connect past efforts from the start of a degradation mechanism that leads to changes in the mechanical strength (such as a reduction in fracture toughness) of pipes and results in crack propagation and leakage. This review focuses on these issues in the context of degradation mechanisms and common detection methods.

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“…In the case of compressed air systems, the most used are ultrasound [8,9] devices and infrared cameras [7]. Although it is considered the industry standard and best practice, ultrasonic leak detection is limited to the application in short distances [10] and requires highly trained operators. Moreover, due to the compressed air expansion process in the location of the leak, a temperature gradient is created, making it possible to observe it from infrared images.…”

Section: Estimating Compressed Leakagementioning

confidence: 99%

“…Among them is the use of ultrasonic detectors [8,9] and infrared cameras [7,8]. Although these methods are the industry standard to locate the leaks, they are impractical [10] for the leakage quantification, particularly in the context of an energy audit. Therefore, an alternative to quantifying the compressed air leakage in a facility is to monitor the flow rate during a period of no compressed air consumption, such as holidays, vacations or weekends [11].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Compressed Air Leakage through Non-Intrusive Load Monitoring Techniques in the Context of Energy Audits

et al. 2022

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Following the recent European directives highlighting the need to increase energy efficiency in the European Union, this work aims to show the possibility of using Non-Intrusive Load Monitoring (NILM) techniques to improve energy audits by estimating the compressed air leakage from a dataset of a tertiary building. The first step towards the reduction of energy consumption is performing an energy audit, in which a detailed analysis of the energy performance is executed. This analysis usually uses on-site measured data by the auditors. However, the time available for these measurements is limited and may not include some modes of operation. One example of that is the quantification of compressed air leaks. This task can be performed by estimating the flow rate during a no compressed air consumption period. However, these periods may not coincide with the auditors’ original schedule. This problem could be addressed by using historical data. Nevertheless, historical data from energy management systems usually are only available for global consumption, and rarely for individual appliances. In this context, a NILM approach would be helpful to enhance energy audits carrying analysis of modes of operation not included in the on-site measurements. In this paper, the leaks are firstly quantified using measurements mostly for benchmarking purposes. The results suggested 62% of leaks in the study case. In a second step, the Factorial Hidden Markov Model (FHMM) was applied to the data. Five typical working days, simulating the context of an energy audit, were used as training data, while one week during vacation time, with no compressed air consumption, was used to quantify the leaks. The results show that it was possible, in the context of an energy audit, to estimate the compressed air leakage using NILM techniques in this dataset with less than a 1% difference when compared to the estimation made with actual measurement. Finally, savings estimations considering the elimination of the leaks were performed, varying between 10% and 100% of the leakage repair. Considering the ideal scenario of complete leaks elimination, the savings would represent around 44% in the compressed air system and 4.75% of the current annual global consumption.

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Test of measurement device for the estimation of leakage flow rate in pneumatic pipeline systems

Cited by 15 publications

References 13 publications

Air leaks fault detection in maintenance using machine learning

Air leaks fault detection in maintenance using machine learning

Degradation mechanisms associated with metal pipes and the effective impact of LDMs and LLMs in water transport and distribution

Quantifying Compressed Air Leakage through Non-Intrusive Load Monitoring Techniques in the Context of Energy Audits

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