Physics‐of‐Failure Approach to<scp>PHM</scp>

Cheng, Shihua; Raghavan, Nagarajan; Gu, Jie; Mathew, Sony; Pecht, Michael

doi:10.1002/9781119515326.ch3

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…failure(s), and monitored over an extended period of time. There are two types of methods to select the monitoring signal: data-driven (Adhikari et al, 2018;Awad et al, 2017;Cheng et al, 2020;Yan et al, 2016) and physics-driven (Pecht and Kang, 2019;Anis, 2018;Glowacz et al, 2017) or hybrid approach of both (Abid et al, 2022). In the former method, data are collected and then critical indicative features are selected and used as inputs to the fault detection model.…”

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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Creation of Publicly Available Data Sets for Prognostics and Diagnostics Addressing Data Scenarios Relevant to Industrial Applications

Fabian

Hagmeyer

Zeiler

2021

IJPHM

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For a successful realization of prognostics and health management (PHM), the availability of sufficient run-to-failure data sets is a crucial factor. The sheer number of given data points holds less importance than the full coverage of the potential state space. However, full coverage is a major challenge in most industrial applications. Among other things, high investment and operating costs as well as the long service life of many technical systems make it difficult to acquire complete run-to-failure data sets. Consequently, in industrial applications data sets with specific deficiencies are frequently encountered. The development of appropriate methods to address such data scenarios is a fundamental research issue. Therefore, the purpose of this paper is to provide facilitation for this research. Accordingly, the paper starts by specifying the value and availability of data in PHM. Subsequently, criteria for characterizing data sets are defined independent of the actual PHM application. The criteria are used to identify typical data scenarios with specific deficiencies that possess significant relevance for industrial applications. Thereafter, the most comprehensive overview of data sets suitable for PHM and currently publicly accessible is provided. Thereby, not all previously identified data scenarios with their specific deficiencies are addressed by at least one data set. A program is established for the aforementioned facilitation of further research. One objective of the program is to create data sets reflecting these data scenarios using a test bench. First, possible applications and their degradation processes to be studied on the test bench are briefly characterized. Thereby, the final decision to select filtration as a test bench application is argued. Subsequently, the test bench created is introduced, including a description of the functional concept, pneumatic layout and components involved, as well as the filter media and test dusts employed. Typical run-to-failure trajectories are illustrated. Thereafter, the data set published under the name Preventive to Predictive Maintenance is presented. Additionally, a schedule for future releases of data sets on further industry-relevant data scenarios is sketched.

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Physics‐of‐Failure Approach toPHM

Cited by 2 publications

References 36 publications

Air leaks fault detection in maintenance using machine learning

Air leaks fault detection in maintenance using machine learning

Creation of Publicly Available Data Sets for Prognostics and Diagnostics Addressing Data Scenarios Relevant to Industrial Applications

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