<p>This master’s thesis work was performed at Volvo Powertrain in Köping, Sweden, which manufactures gearboxes and integrated transmission systems for heavy vehicles. The thesis is a continuation of a previous master’s thesis performed at the Köping factory in 2009. After manufacturing and assembly, each gearbox is manually validated to ensure the gearbox quality and functionality. When validating the gearbox gears, the operator shifts the gearbox in a predefined manner and listens for irregularities. If an error sound is heard the operator must then locate the source of error. With numerous of cog wheels rotating at the same time this task requires extensive knowledge and experience of the operator. The main objective is to develop an automatic diagnostic system for detection of cog errors and assist the operator in the process of locating the faulty component.</p><p>The work consists of two parts. In the first part the automatic diagnostic system is developed and a database of gearbox recordings is stored. The amounts of logged non-faulty gearboxes are significantly much larger (50) than the logged faulty gearboxes (1). Therefore, when determining thresholds needed for the diagnosis, the data obtained from the non-faulty gearboxes are used. Two statistical methods are presented to extract the thresholds. The first method uses an extremevalue distribution and the other method a Gaussian distribution. When validated, both methods did successfully detect on cog faults. In the second part an investigation is made of how shaft imbalance can be detected and implemented in the developed system.</p><p>Volvo Powertrain continually follows-up all faults found at the validation station to ensure the quality of their work and eliminate the sources of error. During system testing one logged gearbox was found faulty. The automatic diagnostic system did successfully detect and locate the faulty component which later also was confirmed when the gearbox was dismounted. With only one detected error it is difficult to conclude the system performance and further testing is required. However, during the testing no false detections were made.</p>

This master’s thesis work was performed at Volvo Powertrain in Köping, Sweden, which manufactures gearboxes and integrated transmission systems for heavy vehicles. The thesis is a continuation of a previous master’s thesis performed at the Köping factory in 2009. After manufacturing and assembly, each gearbox is manually validated to ensure the gearbox quality and functionality. When validating the gearbox gears, the operator shifts the gearbox in a predefined manner and listens for irregularities. If an error sound is heard the operator must then locate the source of error. With numerous of cog wheels rotating at the same time this task requires extensive knowledge and experience of the operator. The main objective is to develop an automatic diagnostic system for detection of cog errors and assist the operator in the process of locating the faulty component. The work consists of two parts. In the first part the automatic diagnostic system is developed and a database of gearbox recordings is stored. The amounts of logged non-faulty gearboxes are significantly much larger (50) than the logged faulty gearboxes (1). Therefore, when determining thresholds needed for the diagnosis, the data obtained from the non-faulty gearboxes are used. Two statistical methods are presented to extract the thresholds. The first method uses an extremevalue distribution and the other method a Gaussian distribution. When validated, both methods did successfully detect on cog faults. In the second part an investigation is made of how shaft imbalance can be detected and implemented in the developed system. Volvo Powertrain continually follows-up all faults found at the validation station to ensure the quality of their work and eliminate the sources of error. During system testing one logged gearbox was found faulty. The automatic diagnostic system did successfully detect and locate the faulty component which later also was confirmed when the gearbox was dismounted. With only one detected error it is difficult to conclude the system performance and further testing is required. However, during the testing no false detections were made.

Tire-pavement interaction noise is one of the dominant sources of vehicle noise, and one of the most significant sources of urban noise pollution. One critical generation mechanism of tire-pavement interaction noise is tire tread excitation. The tire tread contributes to the tire-pavement interaction noise mainly through two mechanisms: (1) tread block impact, and (2) the compression and expansion of the air in the tread groove at the contact patch. The tread pattern is the critical part of the tire design since it can be easily modified. Hence, the main focus of this study is to quantify the tread pattern contribution in total tire-pavement interaction noise. To achieve this goal, the noise produced by the tread pattern is separated from the total tire-pavement interaction noise. Since the tread pattern excitation is periodic with tire rotation, the noise produced by the tread is assumed to be related to the tire rotation. Hence, the order domain synchronous averaging method is used in this study to separate and quantify the tread pattern contribution to the total tire-pavement interaction noise. The experiment has been carried out using an On-Board-Sound-Intensity (OBSI) system. Five tires were tested including the Standard Reference Test Tire (SRTT). Compared to the conventional OBSI system, an optical sensor was added to the system to monitor the tire rotation. The once per revolution signal provided by the optical sensor is used to identify the noise signals associate to each revolution. In addition to the averaging method using optical signals, other data processing techniques have been investigated for separating the tread-pattern noise without utilizing the once per revolution signal. These techniques are autocorrelation analysis, a frequency domain filter, principal component analysis, and independent component analysis. In the tread-pattern noise generation, the tread profile is the most important input parameter. To characterize the tread profile, the tread pattern spectral content and air volume velocity spectral content for all the five tires are computed. Then, the tread pattern spectrum and the air volume velocity spectrum are both correlated with the separated tread-pattern noise by visual inspection of the spectra shape. / Master of Science

This master's thesis deals with order analysis. The first part of the thesis describes common methods for order analysis and methods for processing tacho and vibration signal. The second part contains a brief description of some open source tools for order analysis focusing on Sound and Vibration Measurement Suite (SVMS) package for LabVIEW by National Instruments company. The main purpose of the thesis has been designing and realization of own library for order analysis as a plugin for development tool LabVIEW. In the library, there are implemented methods for order analysis including basic functions for processing vibration and tacho signal. Their brief description with manual for its usage is in the third part of the thesis. In the last chapter, implemented functions are compared with those, which are implemented in SVMS by National Instruments, using both simulated and real data.