TY - GEN
T1 - Vibration Condition Monitoring of Rotating Machinery with IoT and Smartphone Sensors
AU - Hafizh, Hadyan
AU - Ali, Mohamad Nazmeer Nazir
AU - Abdul Majeed, Anwar P.P.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - This study proposes an Internet of Things (IoT) platform designed for real-time monitoring of rotating machinery. Accelerometer sensors embedded in an Android-based smartphone are used to collect vibration data from the rotating machinery. The collected data is then published to a public MQTT broker through a custom-built IoT platform. Subsequently, the data is visualized in a series of dashboards using Node-RED, allowing for real-time monitoring of the machinery’s condition. To ensure the validity of the collected data, a comparison is made between the pre-defined frequencies of the machine and those calculated using the Fast Fourier Transform (FFT) method. The results demonstrate a strong agreement between these two sets of frequencies, confirming the capability of the developed IoT platform to accurately sense and capture the correct data. Additionally, this study includes the development of an early warning system as part of a predictive maintenance framework. The results showcase the effective functioning of the early warning system in promptly alerting the user when the specified trigger condition is met. The integration of IoT technology with a predictive maintenance framework enables advanced detection of equipment failures, highlighting the potential benefits of the proposed method.
AB - This study proposes an Internet of Things (IoT) platform designed for real-time monitoring of rotating machinery. Accelerometer sensors embedded in an Android-based smartphone are used to collect vibration data from the rotating machinery. The collected data is then published to a public MQTT broker through a custom-built IoT platform. Subsequently, the data is visualized in a series of dashboards using Node-RED, allowing for real-time monitoring of the machinery’s condition. To ensure the validity of the collected data, a comparison is made between the pre-defined frequencies of the machine and those calculated using the Fast Fourier Transform (FFT) method. The results demonstrate a strong agreement between these two sets of frequencies, confirming the capability of the developed IoT platform to accurately sense and capture the correct data. Additionally, this study includes the development of an early warning system as part of a predictive maintenance framework. The results showcase the effective functioning of the early warning system in promptly alerting the user when the specified trigger condition is met. The integration of IoT technology with a predictive maintenance framework enables advanced detection of equipment failures, highlighting the potential benefits of the proposed method.
KW - Condition monitoring
KW - Internet of Things
KW - Predictive maintenance
KW - Rotating machinery
UR - http://www.scopus.com/inward/record.url?scp=85187776461&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-8498-5_33
DO - 10.1007/978-981-99-8498-5_33
M3 - Conference Proceeding
AN - SCOPUS:85187776461
SN - 9789819984978
T3 - Lecture Notes in Networks and Systems
SP - 421
EP - 431
BT - Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023
A2 - Tan, Andrew
A2 - Zhu, Fan
A2 - Jiang, Haochuan
A2 - Mostafa, Kazi
A2 - Yap, Eng Hwa
A2 - Chen, Leo
A2 - Olule, Lillian J. A.
A2 - Myung, Hyun
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023
Y2 - 22 August 2023 through 23 August 2023
ER -