Design, development and test of the gearbox condition monitoring system using sound signal processing

ماشین های کشاورزی

Article 5, Volume 6, Issue 2 - Serial Number 12, 2016, Pages 322-335   

Document Type: Research Article

DOI: 10.22067/jam.v6i2.47825

Authors

M. Zamani; M. Aboonajmi^* ; S. R. Hassan Beigi Bidgoli

Agro-technology Dept, College of Abouraihan, University of Tehran, Tehran, Iran

Abstract

Introduction
One of the ways used for minimizing the cost of maintenance and repairs of rotating industrial equipment is condition monitoring using acoustic analysis. One of the most important problems which always have been under consideration in industrial equipment application is confidence possibility. Each dynamic, electrical, hydraulic or thermal system has certain characteristics which show the normal condition of the machine during function. Any changes of the characteristics can be a signal of a problem in the machine. The aim of condition monitoring is system condition determination using measurements of the signals of characteristics and using this information for system impairment prognostication. There are a lot of ways for condition monitoring of different systems, but sound analysis is accepted and used extensively as a method for condition investigation of rotating machines. The aim of this research is the design and construction of considered gearbox and using of obtaining data in frequency and time spectrum in order to analyze the sound and diagnosis.
Materials and Methods
This research was conducted at the department of mechanical biosystem workshop at Aboureihan College at Tehran University in February 15th.2015. In this research, in order to investigate the trend of diagnosis and gearbox condition, a system was designed and then constructed. The sound of correct and damaged gearbox was investigated by audiometer and stored in computer for data analysis. Sound measurement was done in three pinions speed of 749, 1050 and 1496 rpm and for correct gearboxes, damage of the fracture of a tooth and a tooth wear.
Gearbox design and construction: In order to conduct the research, a gearbox with simple gearwheels was designed according to current needs. Then mentioned gearbox and its accessories were modeled in CATIA V5-R20 software and then the system was constructed.
Gearbox is a machine that is used for mechanical power transition from a productive source of power to a consumer, for torque meeting and for rotating speed needed for the consumer. In fact, gearbox is an interfere between power source and power consumer which produces a flexible communication between power source and power consumer. Needing to a gearbox as a machine which can generate harmony as an interface is unavoidable due to lack of harmony of torque and rotating speed of production source of power. So necessary calculations in order to attain to technical characteristics of gearwheels, bearings, shaft dimensions and other accessories of gearbox were done. This gearbox is from kinds of simple gearwheel which its input and output shaft are parallel to each other.
Main accessories of gearbox are: 1.crust 2.shaft 3.gearwheel 4.thorn 5.bearing 6.cover. All of the design parameters were calculated and considered in designing of all of the accessories of gearbox.
Electromotor rotating calibration: For this aim, a light-contact telemeter in model of Lutron was used as contact.
Acoustic module of electro motor: A module was constructed in order to prevent from sound waves interaction resulting from an electromotor function with waves of gearbox function. Three layers of sound absorbent including common felt with 1mm width, polyethylene foam with 15 mm width and shoulder foam egg with 35 mm width were used for the module insulation. Material used for the body of this module was MDF. Based on field measurement, level of electromotor sound decrement using the acoustic module was 20dB. Investigated malfunctions in this research are relevant to gearwheel with one tooth fracture, one worn tooth and one tooth fracture and other worn tooth.
Collection and storage of acoustic data: In this research, an audiometer in model of HT-157 made in Italy in order to obtain acoustic data and a laptop with a model of Lenovo-G550 for data storage and processing was used. Cool Edit Pro 2.0 software was used for data processing. Data storage was in PCM format and MATLAB R2014a software used for data processing.
Data processing: Signal processing method in the frequency domain is used in order to reveal the defects.
Fast Fourier Transform: Fast Fourier Transform FFT for application in electronic equipment specially analyzers have great importance. In this condition, sampling number is chosen exponentially as 2N which decreases the calculation volume significantly.
Determination of defect kind of gearwheel using frequency spectrum analysis: In mentioned gearwheel, errors were generated synthetically. Defect kind of these errors was generated in separate gearwheels in order to investigate the defects more precisely and a gearwheel was considered as control gearwheel. Despite of this, the sound of all of the gearwheels in correct condition was stored.
Results and Discussion
Comparison of processed acoustic signals from gearwheels of gearbox in two correct and incorrect conditions was indicative of gearwheel involvement, frequency, their harmony and the changes resulted from defects. Gearwheel defect detection tests showed that at the speeds of 1496, 1050 and 749 rpm, investigated defects are recognizable with a comparison of the frequency spectrum of obtained signals in correct and incorrect conditions and according to the involvement frequency of gearwheel, its harmony and sided spectrum. Results of the frequency spectrum of signal analysis in speed of 1496 rpm pinion showed the defect of one tooth fracture in involvement frequency of gearwheels by 489, 350 and 249 Hz respectively which became apparent with a mentioned frequency domain increment. A worn tooth defect in a gearwheel was completely determinable as sided bands with equal distance around gearwheel involvement frequency in the signal frequency determination of the speeds of 1496 and 105 rpm pinion, but became a bit harder in less speeds. Investigation of frequency spectrum of acoustic signal resulted from gearwheel, is indicative of the ability of this method in gearbox condition investigation with high precision and minimum time. So the gearbox condition investigation is reached by investigation of the frequency spectrum of acoustic signal resulted from gearwheel.
Conclusions
In current research, acquisitive signals resulted from produced sound waves of constructed gearwheel were used for investigation and diagnosis. Recorded signal in time domain and processed frequency and exploited characteristics of signal in frequency domain for diagnosis were analyzed. Obtained results of this research can be summarized as follow:
1. Precision level in the diagnosis decreased by increasing in pinion speed.
2. There will be a decrement in gearwheel diagnosis after defects integration and signal behavior won’t be completely similar to the defect as individual.
3. Proper placement of audiometer is effective in diagnosis trend.
4. In frequency spectrum of obtained signals, particle velocity level is more efficient in diagnosis than the sound pressure level.

Keywords

Condition monitoring; Frequency domain; Gearbox; Sound signal processing

References

1. Ahmadi, H., and K. Mollazade. 2008. A practical approach to electromotor fault diagnosis of Imam Khomaynei silo by vibration condition monitoring. African Journal of Agricultural Research 4: 383-388.

2. Amarnath, M., V. Sugumaran, and H. Kumar. 2013. Exploiting sound signals for fault diagnosis of bearings using decision tree. Measurement 46: 1250-1256.

3. Bartelmus, W., and R. Zimroz. 2008. Belt conveyor driving system vibro-acoustic severity reduction by condition based maintenance. Prace Naukowe Instytutu Gornictwa Politechniki Wrocławskiej. Studia i Materiały 123: 5-16.

4. Dayani, A. 2013. Signals and Systems. Nas. Publishers, Tehran.

5. Ebrahimi, E., P. Javadikia, N. Astan, M. Heydari, M. Bavandpour, M. H. Jalili, and A. Zarei. 2013. Developing an intelligent fault diagnosis of MF285 tractor gearbox using genetic algorithm and vibration signals. Journal of Modern Mechanical Engineering 3: 152-160.

6. Ghasemzadeh, H. 2000. Mechanical design components of the machine. Tabriz University Press. Tabriz.

7. Gol-Mohammadi, R. 2010. Sound and vibration engineering. Student publishers. Hamadan.

8. Hasan Beygi, S. R., B. Ghobadian, and N. Kamalian. 2004. Prediction model of a power tiller noise Level at rural dirt road. Journal of Agriculture Science 10: 47-58.

9. Heydar-beigi, K., H. Ahmadi, S. A. Tabatabai-Far, and O. Mahmud. 2007. Massey Ferguson tractor gearbox vibration condition monitoring. Second technical conference condition monitoring and troubleshooting machines. Sharif University of Technology, Iran.

10. Homai, H., and K. Safarai. 2014. Sound feasibility analysis for troubleshooting a helical gear box with sliding bearings. Fourth International Conference on Acoustics and Vibration. Tehran, Iran.

11. Jiang, J., F. Gu, R. Gennish, D. Moore, G. Harris, and A. Ball. 2008. Monitoring of diesel engine combustions based on the acoustic source characterisation of the exhaust system. Mechanical Systems and Signal Processing 22: 1465-1480.

12. Kanthababu, M., M. Shunmugam, and M. Singaperumal. 2008. Tool condition monitoring in honing process using acoustic emission signals. International Journal of Automation and Control 2: 99-112.

13. Mazrui-Sabdani, M. 2009. EMD new method for troubleshooting gearboxes. 4th Technical Conference condition monitoring and troubleshooting machines. Sharif University of Technology, Iran.

14. Mutabi, H. 2003. Design component of the machine. Ashina. Tabriz.

15. Ravindra, H., Y. Srinivasa, and R. Krishnamurthy. 1997. Acoustic emission for tool condition monitoring in metal cutting. Wear 212: 78-84.

16. Wu, J. D., C. K. Huang, Y. W. Chang, and Y. J. Shiao. 2010. Fault diagnosis for internal combustion engines using intake manifold pressure and artificial neural network. Expert Systems with Applications 37: 949-958.

17. Wu, J. D., and C. H. Liu. 2008. Investigation of engine fault diagnosis using discrete wavelet transform and neural network. Expert Systems with Applications 35: 1200-1213.

18. Zhan, Y., and C. K. Mechefske. 2007. Robust detection of gearbox deterioration using compromised autoregressive modeling and Kolmogorov–Smirnov test statistic-Part I: Compromised autoregressive modeling with the aid of hypothesis tests and simulation analysis. Mechanical Systems and Signal Processing 21: 1953-1982.