Active Control of Forklift Truck Cabin Noise:A Feasibility Study and Computer Experiments

Sven Johansson

Department of Telecommunications and Signal ProcessingBlekinge Institute of Technology

SE-372 25 Ronneby, Swedensven.johansson@bth.se

Abstract. During the last decade noise has come to be considered a grow-ing problem in our homes, at our work places and also in different kind ofvehicles. The noise level is very important for well being and maintaininga high standard of acoustic comfort. During recent years, however, the noiselevel inside working machines, e.g. forklift trucks, has also been considereda problem. A question is if it may be possible to reduce the low frequencycabin noise by using active noise control, and thereby improving the com-fort for the truck driver. This paper addresses a feasibility study of applyingactive noise control to reduce the noise level inside a forklift truck cabin.The paper discusses the noise situation inside the truck cabin as well as theactive noise control strategy for a practical forklift truck installation. Com-puter experiments performed in MATLAB and based on noise signals re-corded in a truck cabin are presented. The preliminary results show the feasi-bility of using active noise control to attenuate low-frequency cabin noiserelated to the rotation speed of the engine.

1 Introduction

Modern forklift trucks incorporate a wide range of sophisticated conventional passivetechniques to reduce the noise inside the cabin. Today, the interior noise level andother factors causing discomfort to the driver are determining factors in the design ofthese machines. Often machine manufacturers buy machine cabins from a sub-contractor, making it difficult to influence or optimize the cabin design to suit a spe-cial type of application. Nowadays, comfort and low noise level are important salesarguments. As a result, manufacturers of machines invest a lot of work and moneyinto improving the cabin comfort in relation to their own products.

Fig. 1. A forklift truck similar to the one used in this project.

Solving noise problems by redesigning the cabin is often costly and difficult,so this method is not considered. The common solution today is to isolate outsidenoise from the cabin by using conventional passive noise control methods such assound-absorbing panels inside the cabin, sound-absorbing materials inside doors andwalls, rubber-listed doors and windows and engine exhaust silencers. The engine ismounted on rubber mounts on the chassis. The entire cabin is also freely suspendedusing soft rubber mounts, thereby preventing machine vibrations from reaching thecabin walls and radiating sound into the cabin.

Ordinary passive control methods often have little effect on low-frequencynoise. In order to overcome this problem active control of noise can be used [1], [2].The passive and active methods are thus complementary, and by combining these twotechniques high noise attenuation over a wide frequency range is made possible.

Different control strategies can be used when designing an active control sys-tem. The strategy depends on the application. The primary noise inside the cabinoriginates both of directly radiated noise as well as structure-borne sound. This is oneof the main reasons why an Active Noise Control (ANC) [3] approach has been util-ized in this project. If the noise mainly had primarily been structure-borne, an ActiveStructural Acoustic Control (ASAC) [4] approach might have been more feasible. Inthe first method loudspeakers are often used to control the sound field, while in thelatter the structure is controlled in such a way as to prevent sound radiation. Errormicrophones are used in both methods to observe the sound field.

The fork lifter used in this project is a prototype though it is similar to the onedepicted in Figure 1. The weight of the truck is approximately 15 500 kg, and theinterior size of the cabin is approximately 1.05 m long, 1.25 m wide and 1.43 m

high. The lift capacity of the truck is 9000 kg, 0.60 m from the center of gravity.The engine is of 85 kW a VOLVO TAD620VE, a straight six turbo charged engine.Figure 2 shows the compartment under the cabin floor. The engine is located behindthe cabin and the gearbox and the 2 hydraulic pumps are located straight under thecabin floor.

Engine

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Fig. 2. The compartment under the cabin floor.

2 The Cabin Noise

In order to determine which means of sound reduction should be applied, it is impor-tant to have a good understanding of the origin of noise. For this reason, a series ofmeasurements was carried out. The measurements were made for three different enginerotation speeds: 1500, 1900 and 2340 rpm, where 2340 rpm is the maximal rpm.

From an active noise control point of view it is rather important that the spec-trum of the sound pressure is dominated by low frequency noise, if the ANC system isto have any effect, i.e. attenuate, the total sound pressure level as measured in dBA.Figure 3 shows the power spectrum for the microphone in the position of the driver'shead. During the measurements the forklift truck was not running and the enginerotation speed was 1900 rpm. There is evidently a lot of energy concentrated in thefrequency range below 500 Hz, which is advantageous from an ANC point of view.

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Fig. 3. (a) The power spectrum of cabin noise at driver's head position (1900 rpm), (b)zoomed spectrum in the frequency range up to 500 Hz.

In choosing the control strategy for the ANC system, it is of outmost importance thatthe dominant noise components and their sources are identified. The main source ofnoise inside the cabin is the engine of course, but the engine fan, the hydraulicpumps, the gearbox and the cardan shaft are also contributory factors. Large peaks can

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Fig. 4. Power spectrum of cabin noise for three different rpm:s. The arrows indicate the3rd order.

be seen at the frequencies 16, 31, 95, 380 Hz originating from the engine orders 0.5th,1st, 3rd and 12th respectively. Here the engine was running at 1900 rpm. The 3rd ordercorresponds to the engine firing frequency, i.e. 3 cylinder explosions per revolution.The frequency peak at 37 Hz represents the fundamental rotation frequency of the fanshaft. There are still some large peaks the origins of which have not yet been identi-fied. More work is required to identify all interesting frequencies and possible noisesources. Different methods can be used to identify the noise sources [5], [6].

Figure 4 shows noise spectrum for three different engine rotation speeds: 1500,1900 and 2340 rpm. As a reference frequency the 3rd order is marked. This figureshows that different engine orders dominate the noise at different rpm:s. Figure 5shows the engine rpm variation over time when the fork-lift truck is driven through acertain test cycle. This test cycle includes both driving the truck with and withoutload on the forks as well as lifting the goods up and down. The test is standardized andshould ideally represent ordinary operating conditions. Here it is shown that thechanges in rpm occur very rapidly, necessitating an ANC system with high trackingcapacity. An adaptive feedforward control strategy is recommended for robust controlof such a non-stationary sound field where the primary sources of noise have beenidentified.

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Fig. 5. Variations in the rpm during an ordinary running cycle.

3 Preliminary Results

The requirement for feedforward active noise control is to detect all sources generatingnoise and then obtain a reference signal from each source. It is clear that the chosenreference signals need to be reasonably well correlated to the noise components to bereduced since the loudspeaker input signals are derived by filtering the reference sig-nals. The maximum attenuation (dB) of noise at the frequency f can be predicted by[32], [5]

A f fdx( ) log ( )= − −( )10 1102γ (1)

where γdx f2 ( ) is the ordinary coherence between the selected reference signal x and the

noise d ( 0 12≤ ≤γdx f( ) ). However, for a multiple reference control system the multi-

ple coherence function is used [32], [5].A coherence of γdx f2 0 9( ) .= indicates a maximum theoretical attenuation of 10

dB. This maximum theoretical attenuation is defined by frequency domain considera-tions; the constraint of causality is not taken into account. Thus, in practice, thecontrol filter must be causal. For these reasons, the actual attenuation measured inreality will always be less than the calculated maximum attenuation [5].

Three different possible reference signals were recorded; two accelerometer refer-ence signals and one tachometer signal. One accelerometer was mounted on the enginebearer and measured the vertical engine vibrations, and one was on the rear cabin

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Fig. 6. Ordinary coherence between the engine accelerometer and the cabin noise (1500rpm).

window. The window accelerometer was chosen with the purpose of finding a referencesignal whose contents were not restricted to the distinct engine vibrations. However,this paper focuses on the accelerometer sensors only. Figure 6 shows the ordinarycoherence γdx f2 ( ) between the engine accelerometer and cabin noise measured in one

of the error microphones, and Figure 7 shows the ordinary coherence between thewindow accelerometer and the same error microphone. The coherence is calculated for1500 rpm.

For the accelerometer at the engine and window high coherence was obtainedat the engine orders 1, 3, 12 and 24. For the window accelerometer, however, highcoherence was also obtained at the frequencies approximately to 90, 170 and 270 Hz.Preliminary control results for these reference signals are shown in Figures 8 and 9,where Figure 8 corresponds to the engine reference sensor while Figure 8 correspondsto the window sensor. The figures show the spectrum of the noise at the driver's headposition with and without the active noise control system activated.

For the engine reference sensor, a reduction is obtained at the frequencies 75,300 and 600 Hz, i.e. the engine orders 3, 12 and 24. Using the window accelerometerthese frequencies were also reduced, but so also were the frequencies 170 and 270 Hz.Although relatively high coherence between the reference signals and the noise wasobtained at some frequency components below approximately 100 Hz, see Figures 6and 7, no reduction was obtained at these frequencies. The limited noise attenuation atthe low-frequency range is related to the loudspeakers used in the experiment. Withthese loudspeakers it was difficult to generate low-frequency noise; in order to improvethe attenuation of these frequency new loudspeakers with better low-frequency charac-teristics must be used. However, to reduce the very low engine orders, loudspeakers

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Fig. 7. Ordinary coherence between the rear window accelerometer and the cabin noise(1500 rpm).

with the range of frequency down to 30 Hz are recommended. Furthermore, other refer-ence sources are also required for higher coherence in the frequency range 100-200 Hz.

In a single-reference system configuration it is difficult to locate the referencesensor at a suitable position so that all frequencies to be controlled have high coher-ence. In a practical installation, however, it is often necessary to use a multiple-reference system to obtain higher coherence [5]. Reference signals from different sen-sors are combined to form a new reference signal. On the other hand, each referencesignal can also be individually processed [32]. The latter method is recommended forcontrol conditions with acoustic beating, i.e. two frequency components close to eachother [7].

To be able to control the frequency components synchronized to the engine ro-tation, the tachometer signal can be used to generate reference signals that contains thesame frequencies as the harmonics to be reduced. Since the gearing between the enginerotation speed, the fan and the hydraulic pump is known, reference signals correlated tonoise produced by the fan and pump can also be created. The reference signal generatedwill have high Signal-to-Noise Ratio (SNR), i.e. they will contain only the tonalcomponents that are to be reduced, and unwanted noise such as measurement noise isvery low. However, the reduction ratio is limited to the reference SNR [32]. The useof reference signals generated in this manner results in higher performance as well asthe ability to control each harmonic individually.The variation in engine rotation speed is continually changing during a working pe-riod, see Figure 5, idle at 760 rpm to a maximum rotation speed of 2430 rpm. Conse-quently, the ANC system must cope with rapidly changing noise. As a result fast

convergence and tracking are crucial prerequisites for successful noise reduction. Therobustness of the adaptive ANC system is also an issue for a well functioning controlsystem, e.g. for factors such as opening and closing the doors and windows.

ANC OFFANC ON

Fig. 8. Control results at 1500 rpm. Reference sensor: engine accelerometer.

ANC OFFANC ON

Fig. 9. Control results at 1500 rpm. Reference sensor: rear window accelerometer.

4 Conclusions

Low-frequency noise is difficult to attenuate by means of passive methods such asenergy absorption because most structures and materials have a small transmissionloss at low frequencies. As a result, active noise control technologies can be used as acomplement to passive methods to improve the attenuation of disturbing low-frequency noise. The preliminary results presented show the feasibility of using activenoise control to reduce low-frequency forklift truck cabin noise. The control simula-tion performed in MATLAB indicates that a feedforward active control system is likelyto introduce a significant attenuation of, for example, low-frequency tonal componentsrelated to the engine rpm. Since it is difficult to locate the reference sensor at a suit-able position so that all frequencies to be controlled have high coherence a multiplereference feedforward control system is recommended. Fast tracking is crucial for awell working ANC system for this kind of application. Future work is to validate theresults in practical experiments in a forklift truck cabin mock-up and then in a cabinmounted on a forklift truck.

Acknowledgements

The project is sponsored by the Foundation for Knowledge and Competence Develop-ment (KK-foundation). The author would like to thank the KK-foundation for thefinancial support. The author would also like to thank Kalmar Industries in Ljungby,Sweden, for all their help during the experimental work carried out in one of theirforklift trucks.

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