Condition monitoring of Industrial machine’s transmission elements – MSDF feature extraction approach

ABSTRACT

Predictive maintenance is a modern Industry 4.0 strategy in which machines are continuously monitored to detect flaws and prevent breakdowns before they occur. A single sensor focuses on a single variable while neglecting larger information components, resulting in poor data quality and an increased risk of issues in critical rotating equipment. Multi-sensory configuration technology has been developed to collect huge amounts of data from a machine in order to enhance monitoring capabilities in terms of precision, resolution, efficiency, resilience, and trustworthiness of the overall system. The goal of this work is to provide an integrated perspective on machine monitoring using the Multi Sensor Data Fusion (MSDF) technique. On four fault bearings, a case study contrasts the results of single and multiple sensors. A feature-level data fusion method is used, in which computations using time-domain vibration signature data are utilised to build a fusional vector, which is then classified using SVM and analysed with Gaussian kernels. The experimental results suggest that the proposed Gaussian kernel with SVM technique outperforms single sensor data interpretation in terms of classification accuracy and generalisation capability. It is an efficient way for finding defects in rotating machinery in excessively noisy environments.

KEYWORDS:

• Multi-sensor
• data fusion
• Predictive maintenance
• Industry 4.0
• data acquisition
• Sensors
• vibration signature

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The author confirms that the data supporting the findings of this study are available within the article. Any other data, if necessary, could be available by corresponding author on reasonable request.

Additional information

Notes on contributors

Nitin D. Pagar

Nitin D. Pagar is working as a associate professor in Mechanical Engineering Department of SOES, MIT Arts, Design & Technology University, Pune. He has completed his doctoral degree in Mechanical and Materials Technology from the Department of Technology, SPPU, Pune. He has about blend of 22 years of Teaching/Research/Industrial experience. His area of interest includes Structural Dynamics, Multi- Attributed Decision Making (MADM) Parametric Optimization, Stress Analysis, Materials and Design. He is Permanent member of ISTE and Indian Institute of Metals (IIM). Author also working as a reviewer of many international peer-reviewed indexed international journals and conferences.

Pratap N. Deshmukh

Pratap N. Deshmukh is working as a Advisory Scientist at IBM Research, Albany, NY, USA. He has completed his doctoral degree in Mechanical Engineering from North Carolina Agricultural and Technical State University, Greensboro, NC, USA. He has about blend of 17 years of Teaching/Research/Industrial experience. His area of Interest includes Materials Science, Manufacturing Engineering, Nano Engineering, Semiconductor technology. He is Permanent member of ISTE.