Real Time Monitoring System for Automotive Tire Set using an

Acoustic Signal

C. J. Chen

, L. S. Chao

, T. H. Li

, C. W. Hsu

, S. K. Chiu

, C. H. Su

, L. H. Huang

, S. Y. G. Tai

Y. C. Chuang

, C. S. Gong

, C. W. Su

and F. Y. Su

Department of Engineering Science, National Cheng-Kung University, Tainan City, Taiwan

Chiu Chau Enterprise Co., LTD, Taoyuan City, Taiwan

Signal Technology Instrument Inc., Taoyuan City, Taiwan

Department of Electrical Engineering, Chang Gung University, Taoyuan City, Taiwan

Chain Young Co., Ltd, NewTaipei City, Taiwan

Key

words: Automotive Tire Set, Oder Tracking Technique, Acoustic Signal.

Abstract: In this research, we present a real time monitoring system for Automotive Tire Set (ATS) using an acoustic

signal. Order tracking techniques applied in signal processing module by using the recursive Kalman

adaptive filtering algorithm which can be used to exact the order amplitudes of global acoustic signals. The

detective system combines a signal processing module and orders amplitude calculated of feature extraction.

On the basis of acoustic signal extraction to distinguish between normal and abnormal types can be used to

acquire high resolution amplitude of different orders. The diagnostic process is based on recording the drive

shaft of tire speed. This system is implemented on the platform of National Instruments (NI) compact-RIO

and c-series modules. Instantaneous shaft speed and acoustic signal are respectively detected by means of

fiber optical and array microphone. Experimental results were carried out for a practical Mitsubishi Freeca

Car to appear obviously the effectiveness of the proposed system in tracking the ATS orders with high

recognition rate. An intelligent prediction integration system with internet (IPII) for ATS was proposed.

1 INTRODUCTION

In recent years, issues like green energy and Internet

of Vehicles (IoV) (He et al., 2014) developed with a

large amount sensors to detect the predictive faults.

The firms may achieve advantages of fast

production, small-volume production of a wide

range of different items, and great cost reduction,

etc., which are the promotion cores of Taiwan

government’s Productivity 4.0 plan The IoV concept

is also included in it, they called Internet of

Everything (IoE). The vehicles are now becoming

more and more complex with increased reliability on

electronics and on-board computers. Hence, fault

diagnosis on engines of vehicle has become

increasingly challenging with a great number of

parts and systems. Therefore, the job of fault

diagnosis of vehicle has become more difficult,

particularly for nonroutine faults. Automotive

manufacturers develop a new electronic diagnostic

systems which can help lead quickly to the root of

vehicle faults. Because of the limited resources of a

vehicle (less information storage and slow

processor) for electronic diagnostic apparatus, it is

difficult to do much more than limit type

diagnostics. Advanced signal processing techniques

are employed and expanded as signal

transformations or machine learning techniques.

Generally speaking, procedure of operating on

internal combustion (I.C.) engine belongs to rotating

machinery in mechanical system. The classifications

of fault in rotating mechanical system are resonance,

bearing fault, power unbalance, gear fault, and so

forth. Recently, fault diagnosis of rotating

machinery in automobile is based on shaft speed

signal and vibration signal to monitor the conditions

of system. The vibration energy or pressure energy

are often exploited in fault diagnostic system which

can be monitored the conditions of vehicle engine

when damage is increased. On the other hand, the

acoustic energy produced by rotating machine

usually reflects the working condition. An

168

Chen, C., Chao, L., Li, T., Hsu, C., Chiu, S., Su, C., Huang, L., Tai, S., Chuang, Y., Gong, C., Su, C. and Su, F.

Real Time Monitoring System for Automotive Tire Set using an Acoustic Signal.

In Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2016), pages 168-173

ISBN: 978-989-758-185-4

experienced technician can make the troubleshooting

by means of listening to the sound of machine. A

diagnostic system using acoustic energy will directly

diagnose in rotating machine. In order to obtain the

useful information by using an acoustic energy for

feature extraction is based on signal processing

techniques when the heavy noise is increased around

the rotating machinery.

The features of fault diagnosis in an I.C. gasoline

engine can be monitored by measuring the vibration

signal, acoustic signal or pressure signal at specific

locations, e.g. engine mounts. Among these signals

mainly comprised a basic frequency with related to

harmonic frequencies, most of which correlates with

revolution of crankshaft of engine. The acoustic

energy or vibration energy is exceptionally increased

when the translation systems are harmed. In general,

a traditional diagnosis method is used to observe the

amplitude difference of energy in time-frequency

domain for fault diagnosis. For instant, the fast

Fourier transform (FFT) methods are used to

observe the amplitude difference in time-frequency

domain at the fixed speed. However, the information

thus obtained is only partial because some features

of fault do not respond significantly at the fixed

operation speed. Hence the smearing problem

generally arises in practical implementation

particularly at various speeds.

A well-known approach is also utilized, the order

tracking technique that exploits acoustic energy or

vibration signals, supplemented with information of

crankshaft speed, serves as a useful tool for

diagnosis of the vehicle engine. The comparison

between frequency analysis and order analysis is

summarized in Table 1. In general, conventional

methods of order tracking are mainly based on a

Fourier analysis. A high resolution order tracking

technique is proposed in order to overcome the

smearing problems arising. Recently, fault diagnosis

of order tracking technique has become one of the

significant approaches in rotating machinery. Using

the order tracking technique can provide the feature

of order spectra from vibration signal and shaft

speed for an I.C. engine. Moreover, an order

spectrum gives the amplitude of signal as a function

of harmonic and crankshaft speed (R.P.M.). Order

tracking is mainly used to analyze and track the

energy of order signal from dynamic signal.

However, generally tracking methods are ineffective

for applications including the multiple independent

shaft speeds. For instance, the shaft speed of an I.C.

engine and the speed of cooling fans are

independent. If one calculates the orders based on

either speeds, the signals related to other speeds

would appear as uncorrelated noise and reduced the

tracking accuracy of the results. In order to avoid

aforementioned problems encountered, the

representative model-based methods have been

proposed, such as an adaptive Kalman filtering

method. In 1996, an adaptive filter theory is

published by Haykin (Haykin, 2002), discussed

some conclusions for adaptive filtering

methodologies and order tracking techniques in fault

diagnosis of rotating machinery.

In this work, a high resolution order tracking

fault diagnosis technique (Wu, et al., 2009) is

proposed for tracking the signals of acoustical

energy of an ATS set. This technique exploits

adaptive filtering based on the Kalman filter

algorithm, the proposed methods also requires the

information of shaft revolution of ATS and

measured by a fiber optical sensor. In this technique,

order amplitudes are calculated off-line by using a

least-squares approach. The adaptive algorithm is

essentially sample-based and the order amplitudes

can be calculated in a real-time fashion for fault

diagnosis of ATS. The technique is implemented on

a NI cRIO 9075 platform for evaluating the

performance in practically diagnostic systems. On

the other hand, the information of vibration signal is

the most widely used in the diagnostic analysis of

fault. In the case of fault diagnosis application

systems by using the vibration reference signal may

not be available. As a result of effect of uncertain

conditions around the vehicle ATS can likely be

generated as the additional vibration delivered from

the ground. Thus, a sound acoustical signal provides

accurate information to the fault diagnosis system.

This research scope is mainly a solution for ATS,

which integrates the sensing and predicting

technology of rotary equipment, order analysis

theory, NI cRIO embedded hardware, Real-Time

Module, FPGA, and PC-Based Guide User Interface,

etc., and the major purpose is to provide the real-

time statuses of rotary equipment. This paper issued

an Intelligent Prediction Integration System with

Internet, IPII. ATS set play a significant role in

vehicle, therefore a sudden broken is unfavourable;

and the intelligent sensing system application can

not only maintain the operating conditions at the

best status to hold life of people. Figure 1 is the

simulation fixture of an intelligent sensing module

system.

For the proposed method, a sound acoustical

signal is exploited to evaluate the proposed

algorithms. The results indicate that the proposed

method is well suited for the tracking of closely

spaced orders or crossing orders without significant

Real Time Monitoring System for Automotive Tire Set using an Acoustic Signal

169

smearing problems. Experiments are carried out to

evaluate the proposed system with practically

running tests under fixed revolution conditions The

filtering algorithms and the order tracking

techniques will be described.

Table 1: Relationship of frequency and order analysis.

Frequency analysis Order analysis

Time [sec] Revolutions [rev]

Frequency [Hz]

[per sec]

Harmonics [Order]

[per revolution]

Figure 1: Simulation fixture of ATS.

2 THEOREY OF ORDER

TRACKING

The speed, acoustics, and vibration signals of rotary

machinery must be calculated, then one transfers the

discrete time signals to discrete angle signals.

Discrete time signals are acquired according to equal

time intervals while discrete angle signals are

sampled by equal shaft angles, and this is the so-

called Resampling theorem. Angle-Samples can be

acquired by hardware devices or software post-

processing.

The hardware solution can achieve the equal

angle interval sampling purpose by using Encoder or

Tachometer’s impulse signals to trigger Analog-to-

Digital Conversion. And this sampling method

requires extra hardware devices and complex-

numbered analog filters, which is not easy to

implement. Hence this paper adopted the software

post-processing method for the experiment. The

method first collects vibration signals and

tachometer’s impulse signals at the same time to

interpolate or Curve-Fitting tachometer’s impulse

signals to receive relative angular displacements at

each time point, then to sample vibration and

acoustic signals based on the equal angular

displacement principle.

The resampled signal are called Order Signal,

and the spectrogram acquired from having FFT or

STFT on Order Signal is called Order Spectrogram

which enables one to easily tell Fundamental

Frequency and Harmonies from the analyzed signals,

and helps to analyze acoustic signal features.

2.1 Kalman Filtering

The signals generated by rotary machinery are

Frequency-Modulated Signal, which usually

contains various frequency elements (main rotary

frequency or its harmony wave). Therefore, signals

can be presented by Superposition with sinusoids of

different frequencies, and we used adaptive Kalman

filter to implement big data computing of rotary

equipment, and the Data Equation is the part of

sensed speed, acoustics data or estimated energy, the

flat condition of estimating order is called Structural

Equation. An Kalman filter algorithm presented

solutions by using State-Space, and is also able to be

resolved by recursive method.

The acoustic signal

()

containing k orders

generated by the rotating shaft from an engine

platform can be expressed as

112 2

() cos[ () ] cos[2 () ] cos[ () ]

xt A t A t A k t













  

(1)

where A

and



express the amplitude and phase,

respectively, of the kth order, and the variable

()t



denotes the angular displacement of the shaft

computed by the following integral

() ( ) 2 ( )

tdfd



    





(2)

where

()





represents the instantaneous angular

frequency (rad/sec) of the ATS shaft which should

be calculated by numerically integrating the

instantaneous angular frequency measured by the

fiber optical sensor from ATS shaft. The block

diagram representation of the adaptive Kalman

filtering is shown as Figure 2, which the procedure

is summarized as follows

Figure 2: Block diagram of Adaptive Kalman filtering.

)(ny

)1(



)(

)(nx

)(

1

)(nC

),1( nn





VEHITS 2016 - International Conference on Vehicle Technology and Intelligent Transport Systems

170

The procedure is summarized as follows

)(),1()(),1(),1(

nnnnnnnn

QFKFK 

(3)

The equation (3) can be represented a Riccati

equation solver for computing the updated value

(1,)nnK

. To initialize the Kalman filtering process,

the initial conditions are generally taken to be:

1)24(0

)y|1(





(

42k 

is the number of

parameters

)(nA

)0,1(

(

is a

(

42k 

)



(

42k 

) identity matrix. Thus in

applying this order tracking method, different shaft

speed of axles must be available. However, the prior

information of the number of order and resonance

frequency is also required. Technically, this can be

obtained by a preliminary scan by using

conventional order tracking methods. If this can be

done, the proposed technique should provide results

with improved accuracy for prediction system on a

ATS. The IPII monitoring structure of ATS is shown

as figure 3.

Figure 3: Structure of an IPII.

3 RESULT AND VERIFICATION

The research is mainly based on applying multi-

sensor big data algorithm, and uses acoustic, speed,

and temperature, etc. to sense signals to extract and

analyze then determine, figure 4 shows the

intelligent ATS experimental structure which

includes sensors from ATS system, fiber optical

tachometer to microphone and the system module is

an embedded 667MHz CPU, with an adaptive

Kalman filter for kernel algorithms.

The IPII system was set to sample frequency at 5

KHz; the extraction time was 12 seconds; FFT

Record Length was 2,048 points; Hanning window

function. Figure 5 is the user interface which can

Figure 4: Intelligence ATS structure.

Figure 5: IPII GUI.

operate the ATS equipment on CPU end, and is able

to link to tablets and continue serving. Figure 6

shows the fiber optical from the ATS condition, the

on-line microphone measures the feature shown as

figure 7-8. Figure 9 is shown as the time signal of

practical microphone.

All the sensors with the network device, as well

as a microphone to perform big data real-time

computing, there was an axle failure, thus figure 10

shows the big data computing result of the acoustic

energy; the solid line is about normal energy

distribution condition of the ATS condition.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

-5

-4

-3

-2

-1

Figure 6: ATS signal from fiber optical.

Real Time Monitoring System for Automotive Tire Set using an Acoustic Signal

171

This paper applied an adaptive algorithm, and

with the network device, one can learn real-time

condition of the current rotary equipment, then to

monitor, predict, and adjust the equipment status in

time, which also meets the predicting and sensing

technology of Taiwan’s Productivity 4.0 plan.

0 1 2 3 4 5 6

x 10

Figure 7: ATS signal from fiber optical.

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

100

120

Figure 8: FFT spectrum with microphone.

0 1 2 3 4 5 6

x 10

-0.8

-0.6

-0.4

-0.2

0.2

0.4

0.6

0.8

Figure 9: Time signal from microphone.

0 2 4 6 8 10 12

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

Normal

Unnormal

Figure 10: Adaptive Kalman filtering algorithm by using

acoustic energy (the dotted line represents a failure

condition while the solid line represents a normal

condition).

4 CONCLUSIONS

A real time monitoring system for Automobile Tire

Set (ATS) using an acoustic signal was proposed.

The point of Taiwan’s Productivity 4.0 plan is how

to plan prediction technology, smart factory, IoT,

and IoV; distributing predicting sensors and the

function of real-time computing and predicting are

implemented in this paper, also, by using intelligent

LAN devices, one can instantly monitor the ATS

with IPII.

The practical sensing of acoustic and fiber

optical signals real-time computing and the adaptive

order theory successfully predict the system

condition of the ATS before a breakdown, which

helps the follow-up maintenances and alarm

instruction. This paper brought up intelligent ATS

equipment based on fiber optical and acoustic

signals, which can effectively monitor various

operational conditions of ATS. The IPII system uses

acoustic signals to predict the shap, tire depth

features of ATS, so that the losses can be reduced to

the minimum before the vehicle accidents.

The result of the experiment proved that the

adaptive order analysis method was an effective

diagnosis to be applied on intelligent ATS real-time

prediction; however, the amount of feature energy

value shown by each big data order needs to be

distinguished by applying an intelligent ambiguous

system, and the order sampling number of every

condition must be taken into consideration, five

samples at least, to increase the accuracy of the

identification rate.

ACKNOWLEDGEMENTS

This study was supported by the Ministry of Science

VEHITS 2016 - International Conference on Vehicle Technology and Intelligent Transport Systems

172

and Technology of Taiwan, the Republic of China,

under project number MOST 104-2221-E-182-044.

REFERENCES

He, Y., Xu, 2014. Developing Vehicular Data Cloud

Services in the IoT Enviroment. IEEE Transactions on

Industrial Informatics, vol. 10, No. 2, May.

Haykin, 2002. Adaptive Filter Theorey, Prentice Hall.

America, 4

edition.

Wu, B., Su, H., 2009. An expert system for the diagnosis

of faults in rotating machinery using adaptive order-

tracking algorithm, Expert Systems with Applications,

vol. 36.

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