Misalignment Detection on Linear Feed Axis with FFT and Statistical Analysis using Motor Current
 
More details
Hide details
1
Institute of Production Science, Karlsruhe Institute of Technology, Germany
CORRESPONDING AUTHOR
Mustafa Demetgül   

Institute of Production Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
Submission date: 2021-12-16
Final revision date: 2022-03-23
Acceptance date: 2022-03-25
Online publication date: 2022-04-05
 
 
KEYWORDS
TOPICS
ABSTRACT
The linear feed axes are critical subsystems in many production machines and have important responsibilities such as transporting workpieces and tools in the process. Therefore, the component’s working condition is crucial for the production of high-quality products. Because these systems gradually deteriorate, it is necessary to detect these changes and occurring faults with condition monitoring systems. In this study, the motor current of feed axes is monitored for axis misalignment that occurs during or after assembly. We conduct diagnosis with Fast Fourier Transform (FFT) and statistical methods in order to differentiate different misalignment scenarios and operating constraints of the feed axis. Different states are achieved by simulating left and right axis misalignment and operating the table at different speeds and strokes.
 
REFERENCES (21)
1.
VOGL G.W., CALAMARI M., YE S., DONMEZ M.A., 2016, A Sensor-Based Method for Diagnostics of Geometric Performance of Machine Tool Linear Axes, Procedia Manufacturing, 5, 621–633.
 
2.
ALTINTAS Y., VERL A., BRECHER C., URIARTE L., PRITSCHOW G., 2011, Machine Tool Feed Drives, CIRP Annals, 60/2, 779–796.
 
3.
LEITE V.C.M.N., DA SILVA J.G.B., TORRES G.L., VELOSO G.F.C., DA SILVA L.E.B., BONALDI E.L., DE OLIVERIA L.E.D.L., 2017, Bearing Fault Detection in Induction Machine Using Squared Envelope Analysis of Stator Current, Bearing Technology, London, UK: InTech., DOI: 10.5772/67145.
 
4.
VOGL G.W., DONMEZ M.A., ARCHENTI A., 2016, Diagnostics for Geometric Performance of Machine Tool Linear Axes, CIRP Annals, 65/1, 377–380.
 
5.
CIABATTONI L., FERRACUTI F., FREDDI A., MONTERIU A., 2017, Statistical Spectral Analysis for Fault Diagnosis of Rotating Machines, IEEE Transactions on Industrial Electronics, 65/5, 4301–4310.
 
6.
YU X., DONG F., DING E., WU S., FAN C., 2017, Rolling Bearing Fault Diagnosis Using Modified LFDA and EMD with Sensitive Feature Selection, IEEE Access, 6, 3715–3730.
 
7.
HOANG D.T., KANG H.J., 2019, A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion, IEEE Transactions on Instrumentation and Measurement, 69/6, 3325–3333.
 
8.
MOHANTY A.R., KAR C., 2006, Fault Detection in a Multistage Gearbox by Demodulation of Motor Current Waveform, IEEE Transactions on Industrial Electronics, 53/4, 1285–1297.
 
9.
PUTZ M., TRIMBORN C., NAUMANN C., FISCHER J., NAUMANN M., GEBEL L. ,2018, Sensorless Fault Detection in Linear axes with Dynamic Load Profiles, Procedia Manufacturing, 19, 66–73.
 
10.
VERMA A.K., SARANGI S., KOLEKAR M.H., 2013, Misalignment Fault Detection in Induction Motor Using Rotor Shaft Vibration and Stator Current Signature Analysis, International Journal of Mechatronics and Manufacturing Systems, 6/5–6, 422–436.
 
11.
ALOK K.V., SOMNATH S., MAHESH K., SHREYA B., 2012, Oil Whip Detection Using Stator Current Monitoring, IEEE Symposium on Computers & Informatics (ISCI), IEEE, 119–124.
 
12.
PRADHAN P.K., ROY S.K., MOHANTY A.R., 2020, Detection of Broken Impeller in Submersible Pump by Estimation of Rotational Frequency from Motor Current Signal, Journal of Vibration Engineering & Technologies, 8, 613–620.
 
13.
YANG Q., LI X., WANG Y., AINAPURE A., LEE J., 2020, Fault Diagnosis of Ball Screw in Industrial Robots Using Non-Stationary Motor Current Signals, Procedia Manufacturing, 48, 1102–1108.
 
14.
DAHIYA R., 2018, Condition Monitoring of Wind Turbine for Rotor Fault Detection Under Non Stationary Conditions, Ain Shams Engineering Journal, 9/4, 2441–2452.
 
15.
PUTZ M., TRIMBORN C., NAUMANN C., FISCHER J., NAUMANN M., GEBEL L., 2018, Sensorless Fault Detection in Linear axes with Dynamic Load Profiles, Procedia Manufacturing, 19, 66–73.
 
16.
ZHOU Y., MEI X., ZHANG Y., JIANG G., SUN N., 2009, Current-Based Feed axis Condition Monitoring and Fault Diagnosis, 4th IEEE Conference on Industrial Electronics and Applications, IEEE, 1191–1195.
 
17.
VOGL G.W., DONMEZ M.A., ARCHENTI A., 2016, Diagnostics For Geometric Performance of Machine Tool Linear axes, CIRP Annals, 65/1, 377–380.
 
18.
REUß M., 2017, Modeling Method for Simulation of Assembly Variances, Stuttgart University, Phd Thesis, Stuttgart, Fraunhofer Verlag.
 
19.
WU Z., JIANG H., ZHAO K., LI X., 2020, An Adaptive Deep Transfer Learning Method for Bearing Fault Diagnosis, Measurement, 151, 107227.
 
20.
MOURTZIS D., 2021, Towards the 5th Industrial Revolution: a Literature Review and a Framework for Process Optimization Based on Big Data Analytics and Semantics, Journal of Machine Engineering, 21/3, 5–39.
 
21.
PUTNIK G.D., SHAH V., PUTNIK Z., FERREIRA L.,2021, Machine Learning in Cyber-Physical Systems and Manufacturing Singularity–it Does not Mean Total Automation, Human is Still in the Centre: Part II–I n-CPS and a View from Community on Industry 4.0 Impact on Society, Journal of Machine Engineering, 21/1, 133–153.
 
eISSN:2391-8071
ISSN:1895-7595