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CONDITION MONITORING IN DIESEL ENGINES FOR COLD TEST
APPLICATIONS. PART II: COMPARISON OF VIBRATION ANALYSIS TECHNIQUES
S.Delvecchio1, O.Niculita2, G.Dalpiaz3, A.Rivola4
1Engineering Department In Ferrara (EnDIF), Via Saragat 1, Ferrara,
[email protected], Phone +390532974969, Fax +390532974870. 2Machine tools and tools Department,Technical University “Gh.Asachi” Iasi, Splai
Bahlui No. 61-63, Iasi, Romania, [email protected], Phone/Fax+40.232233924. 3 Engineering Department In Ferrara (EnDIF), Via Saragat 1, Ferrara,
[email protected], Phone +390532974883, Fax +390532974870. 4DIEM, University of Bologna. Viale Risorgimento 2, Bologna, Italy
[email protected], Phone+390512093440, Fax +390512093446 ABSTRACT This work compares the effectiveness of different vibration analysis techniques for fault detection in diesel engines during cold tests for quality control at the end of assembly line. The cold test imposes a pass/fail decision comparing vibration features with proper threshold values. In part I, the authors have described the capabilities of the cold test monitoring and presented a technique based on statistical analysis of the vibration signal in the time domain in order to obtain reliable threshold values. In this part, the effectiveness of some advanced techniques is compared and discussed. Some parameters extracted by processing the PSD (Power Spectral Density) are compared to features obtained from time-frequency analysis techniques (Short Time Fourier Transform and Wavelet Transform) and from image correlations of the symmetrised dot pattern of the vibration signal (SDP). Experiments on 29 engines were carried out and eight different kinds of faults were introduced one by one in engines in order to assess the effectiveness of these techniques. KEYWORDS Condition monitoring, diesel engines, time-frequency techniques, symmetrized dot pattern, image correlation. 1. INTRODUCTION This paper addresses the use of vibration measurements as a means of condition monitoring of diesel engines for quality control at the end of assembly line through the cold test technology. The cold test technology requires a fast and efficient method to extract reliable features from the vibration signature of the engines for the pass/fail decision. In the Part I of this paper [1] this problem is presented and it is proved that the calculation of global parameters which describe the PDF (Probability Density Function) of the vibration signal in the time domain may be used as a correct indicator for the condition monitoring
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procedure. This second part gives emphasis to the comparison of some techniques applied in the frequency and time-frequency domains. Moreover, a technique based on the representation of the vibration signal in polar graphs is developed in this paper. Many researchers have proposed the application of advanced vibration analysis techniques for monitoring and diagnostics of diesel engines. Kimmich proposed a model-based diagnosis method [2]. Antoni ed others [3-4] proposed a methodology based on the properties of cyclostationarity that was applied to malfunctions related to engines in firing (advance and delay of injections, misfires and knocks). The application of time-frequency distribution techniques is well suited for the analysis of transitory signals and has been widely applied to engine monitoring. The Short-Time Fourier Transform (STFT), the Wigner-Ville Distribution (WVD) and the Continuous Wavelet Transform (CWT) were used in order to distinguish faulty conditions from normal ones for practical fault diagnosis and not to obtain reliable parameters for an automatic procedure led by a data acquisition system [5-8]. The aim of the second part of this paper is to assess and compare the detection capability of some of these techniques, on the basis of a large amount of experimental results, in order to implement them for an easy and fast quality control procedure after the cold test. Some approaches which take in account this requirement have been introduced by applying the measurement of instantaneous angular speed for detection of the flank wear in gearboxes and fuel leakage in engines in firing [9,10] and by use of symmetrized dot patterns for the visual characterization of vibration signatures in a fault diagnosis system [11,12]. Hugh Thomas and others developed a diagnostic method to detect engine knock using pattern recognition with wavelet networks [13]. This paper is organized in three sections. In every section each technique is outlined. The experimental results are then presented and the capability of fault detection is discussed. The analysis techniques are applied to the same experimental vibration data concerning the two test investigation described in the first part of this paper, pointing out the effects of choosing different transducer locations. The tests are realized on diesel engines, namely “2.8l”, produced by VM Motori for its customer Chrysler and having the following specifications:
Engine specifications 4 line cylinders Stroke [mm] 100 Bore [mm] 94 Rapport stroke/bore 1.06 Unit displacement [cc] 694 Displacement [cc] 2776 Power max(@ 4000 rpm) [kW] 110 Torque max(@ 2000 rpm) [Nm] 360 Table 1 – Specifications of the 2.8l diesel engine. The cold test bench prototype is designed by Apicom (Cento, Italy) and it is equipped by a data acquisition system produced by Sciemetric and controlled by the QWX software, as said in Part I. In this second part only the FFT data were processed by means of the QWX software, while the other analysis are carried out by means of Matlab software. The experimental arrangement related to two experimental investigations and the simulated faults are described in the companion paper [1]. 2. FREQUENCY ANALYSIS Frequency domain techniques for machine condition monitoring and diagnostics are being used widely for different kinds of machine. In the case of engine condition monitoring for quality control at the end of the assembly line, few applications of these techniques exist. Problems may be arise for the presence of non-stationary dynamic phenomena during the signature vibration acquisition from the engine block: these kinds of technique, which are based on the assumption of stationarity, are not always effective. However, the common acquisition systems exhibit implemented techniques in the frequency domain for processing vibration signals in order to obtain a reliable cold-test pass/fail procedure. Some examples of commonly used techniques in time and frequency domain for automatic machine applications are reported in [13]. With reference to the two experimental investigations described in the first part of this paper the
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results of two basic frequency analyses of the vibration signal are shown in this section. Firstly, a spectral feature implemented in the QWX software is considered; it deals with the area under the Fourier spectrum curve. The spectra are obtained by averaging the FFT amplitude on 7 sets with Hanning window, using bandwidth of 5580 Hz and frequency resolution of 3.48 Hz. Moreover, the mean value of the PSD in the highest amplitude range is calculated. The PSDs (Figure 1) were obtained for the first experimental investigation at both operational speeds by means of the same acquisition parameters used for the spectra calculation. From the graphs obtained at 120 rpm one can notice the clear difference between the normal and faulty conditions in the range 4000-5000 Hz. This PSD analysis carried out at 1000 rpm did not give the same clear results.
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(d) (e) (f) Figure 2– First experimental investigation (120 rpm); PSD of the engine block acceleration in faulty
conditions: (a) Piston inverted, (b) overpressure oil valve inverted, (c) equalizer out of housing, (d) oil pump screw improperly tight, (e) one connecting rod tight with 3 kgm, (f) one oil jet improperly tight.
Because the good discrimination resulted for the first experimental investigation, the PSD analysis is carried out also for the second one in order to assess the method and to evaluate the influence of accelerometer location. Table 2 and Table 3 show the values of area under spectrum curves and the PSD mean values in the range 4000-5000 Hz. Comparing the results of the spectrum areas calculated for the first experimental investigation there is a clear discrimination between normal and faulty conditions for the tests conducted at 120 and 1000 rpm. Thus a reliable upper threshold may be obtained for all the faulty conditions. Regarding the engine with a wrong rod pre-load it is worth noting that there is not a
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great difference between the values in normal and in faulty conditions (67.24 gHz Vs 67.91 gHz). In addition there is not an unique discrimination for both faulty conditions at the same operational speed: one fault is detected at 120 rpm (counter rotating masses) and the other one at 1000 rpm (rod half-bush). Spectrum area [gHz] PSD mean value in
4000-5000 Hz [m2/s3] Engine 120 rpm 1000 rpm 120 rpm
Mean + 3 Sigma for normal engines 3.81 67.24 0.51
Inverted piston 8.71 316.48 4.85 Overpressure valve 6.78 74.19 1.52
Exhaust equalizer out of housing 6.46 75.93 2.08 Oil pump screw improperly tight 6.63 87.97 2.78
One connecting rod tight with 3 kgm 6.14 67.91 2.45 Oil jet improperly tight 6.38 82.25 1.65
Table 2 – First experimental investigation: spectrum area and PSD mean value. Spectrum area [gHz] PSD mean value in
4000-5000 Hz [m2/s3] Engine 120 rpm 1000 rpm 120 rpm
Normal engine 8.35 50.04 0.23 Counter-rotating masses with a phase
lag 13.67 51.81 13.50
Rod without a half-bush 6.96 169 0.93 Table 3– Second experimental investigation: spectrum area and PSD mean value. Concerning the PSD mean value, this parameter seems to be an effective monitoring feature for all the faulty conditions. Regarding the second investigation, where there is not a clear differences between the faulty values and the normal ones (see e.g counter rotating masses with a phase lag) it is not possible to obtain a reliable upper threshold: it is worth noting that only a healthy engine is tested and so the 3 sigma confidence level is not available. In order to overcome the assumption of stationarity about the previous frequency techniques, the application of time-frequency methods is required. 3. TIME-FREQUENCY ANALYSIS 3.1 Formulation and properties In this section two time-frequency domain techniques are evaluated: STFT (Short Time Fourier Transform) and DWT (Discrete Wavelet Transform). In general these methods perform a mapping of one-dimensional signal to a two-dimensional function of time and frequency and do not give directly useful parameters for quality control applications. It is well known that the problems with STFT, which provides constant resolution for all frequency since it uses the same window for the analysis of the whole signal, are overcome by application of the wavelet transform that can be used for a multi-scale analysis: at high frequencies the wavelet reaches at a high time resolution but a low frequency resolution, whereas, at low frequencies, high frequency resolution and low time resolution can be obtained. In this work the STFT is calculated in order to have a time-frequency method easy to be implemented for the application of the pass/fail procedure. The STFT method is illustrated in [5]. Figure 3 shows the STFT spectrogram obtained for a normal and faulty condition (piston inverted). Because of the low sensitivity shown by this technique to the detection of all faulty conditions tested, the DWT technique is applied.
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(a) (b) Figure 3 – STFT -log normalized amplitude (120 rpm): (a) normal condition; (b) faulty condition (piston inverted). As mentioned in [15], if x[n] is the sampled version of original signal x(t), the DWT computes wavelet coefficients and scaling coefficients for j=1,…, J given by:
]2[][, knhnxc j
njkj −= ∑ Eqn. 1
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]2[][, kngnxd j
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where jh ]2[ kn j− is the analysis discrete wavelet and jg ]2[ kn j− is the scaling sequence. From a mathematically point of view, the terms g and h are high-pass and low-pass filters derived from the analysis wavelet ψ (t) and scaling function φ (t). Thus, kjc , represents the high frequency components of the signal x(t) and kjd , represents the low frequency components of the same signal x(t). 3.2 Experimental results Mahjoob and Zamanian (see e.g. [5]) have employed the STFT and CWT (Continuous Wavelet Transform) methods for engine condition monitring. Here, two different methods for feature extraction are obtained for both time-frequency techniques and their sensitivity to faulty condition recognition is then evaluated. Regarding the STFT, the kurtosis coefficient is calculated for both time (Time kurtosis) and frequency distributions (Frequency kurtosis), as shown in Figure 4, and the mean value of this parameter is then obtained. Due to the previous results achieved for the spectra and PSD, the STFT is evaluated for the only 120 rpm operational speed phase. Table 4 shows the STFT results with a Hanning window using bandwidth of 5580 Hz and frequency resolution of 3,48 Hz: one can notice that the mean time kurtosis parameter is more sensitive than the mean frequency one for faulty discrimination purposes.
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Figure 4 – Normal condition at 120 rpm: (a) kurtosis of the time distribution and (b) the frequency distribution; faulty condition at 120 rpm (piston inverted): (c) kurtosis of the time distribution and (d) the
frequency distribution. Regarding the first investigation, an upper threshold may be obtained only for the piston inverted engine while both faulty conditions may be recognized in the second investigation at 120 rpm. From these results this method does not seem to give an acceptable stability to the discrimination of all faulty conditions tested from the normal ones. Kurtosis of the time-frequency distributions at 120 rpm
Engine Mean time kurtosis Mean frequency kurtosis Mean + 3 Sigma
for normal engines 13.86 45.35
Inverted piston 24.28 6.14 Overpressure valve 4.67 6.8
Exhaust equalizer out of housing 4.76 7.41 Oil pump screw improperly tight 4.67 5.96
One connecting rod tight with 3 kgm 8.53 7.37 Oil jet improperly tight 6.99 7.17
Table 4 – First experimental investigation: kurtosis mean value of the time and frequency distribution. Kurtosis of the time-frequency distributions at 120 rpm
Engine Mean time kurtosis Mean frequency kurtosis Normal engine 13.18 7.81
Counter-rotating masses with a phase lag 22.138 8.47
Rod without a half-bush 45.86 12.039 Table 5– Second experimental investigation: kurtosis mean value of the time and frequency distribution. In order to overcome this problem, the DWT technique for the extraction of faulty components from the signal, proposed by Shibata [12], is evaluated. Figure 6 shows the coefficients of the DWT ( kjc , ) when Symlet (eight order) is used for wavelet and the scaling function. Data sampled at 70 μs were used for DWT.
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Figure 6 – Coefficients of DWT for the vibration signals (120 rpm): (a) Normal condition; faulty condition (piston inverted).
Table 6 shows the comparison with the ratio of RMS of the DWT coefficients in faulty (F) and the normal (N) conditions (one of 21 healthy engines tested) for both investigations at 120 rpm. All the levels are sensitive to the presence of the fault for every faulty engine; moreover at level j = 1 it can be seen the higher difference between the faulty and the normal vibration signals. Thus the RMS ratio at the first decomposition level may be considered a reliable monitoring feature.
Level kjC , Ratio of RMS F/N Inverted piston
Ratio of RMS F/N Op valve
Ratio of RMS F/N Equalizer
Ratio of RMS F/N Oil pump screw
- 1.11 1.12 3.04 1.20 j = 5 1.01 2.18 1.68 1.36 j = 4 1.60 2.26 1.44 1.14 j = 3 2.26 1.97 1.47 1.28 j = 2 3.50 2.19 1.88 1.96 j = 1 3.76 3.57 3.61 3.36
Level kjC , Ratio of RMS F/N
Rod 3kgm Ratio of RMS F/N
Oil jet Ratio of RMS F/N
Masses Ratio of RMS F/N
Rod half-bush - 1.15 1.08 1.04 1.69
j = 5 1.29 1.65 1.34 1.78 j = 4 1.64 1.67 0.94 1.33 j = 3 1.64 1.74 0.68 1,20 j = 2 1.76 2.07 1.16 1,63 j = 1 2.87 3.29 4.69 1.75
Table 6 – Comparison with coefficients of DWT: ratio of RMS value between the faulty (F) and normal (N) conditions. 4. IMAGE CORRELATION 4.1. Symmetrized dot polar graphs and image correlation: formulation and properties As presented in Ref. [11], the normalized time histories of the vibration signal concerning diesel engines can be represented as symmetriezed dot graphs. The formulation used for the transformation of the discrete signal x(n) to a polar coordinate graphs and the properties of this method are described in Ref. [12]. In this work the time lag L and the gainζ present the following values: L=1, ζ =60°.
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Figure 7 – Normal pattern: (a) time input waveform; (b) symmetrized dot polar graph and (c) image obtained after the edge detection algorithm application.
In order to implement this technique in the cold test procedure for fault detection, it is necessary to develop an image correlation system. The authors applied the algorithm of the edge detection, illustrated in [17], which represents the most common approach for detecting meaningful discontinuities in intensity values. The basic idea behind the edge detection is to find the points where the intensity rapidly changes. For each case, by applying this edge detection algorithm on the image (visual dot symmetrized polar graph) the result will be a logic matrix with 1s (represented as white pixels in the grey scale) at the locations where edge points were detected in the image, and 0s (black pixels) elsewhere (Figure 7 (c)). The goal is to identify a pattern that represents the normal condition and then to match the images obtained from all the faulty engines with this ‘normal pattern’. It will be considered a correlation threshold between the normal engines and faulty engines. One solution was identified in considering this limit of similarity of two images (normal pattern and faulty images) by calculating the percentage of common white pixels with respect the total number of white pixels in the normal pattern. 4.2. Experimental results First of all, the limit of similarity of two images (both normal engines) is calculated for the group of healthy engines. In order to obtain the normal pattern image, the similarity comparison between normal engines is evaluated: the engine that presents the minimum limit of similarity with each other (correlation threshold: UC = 25.08%) is assumed as the pattern, unique for both investigations.
(a) (b)
Figure 8 – Symmetrized dot polar graphs: (a) normal pattern; (b) oil pump screw improperly tight, (c) engine a rod tigh with 3 kgm.
This pattern is compared with the images obtained from the faulty engines and so the limits of similarity are calculated and verified if they are lower than the correlation threshold (CT) in order to discriminate the faulty condition from the normal one. Table 7 shows that all the faulty engines give a percentage of common white pixels lower than the healthy engines; so it is possible to obtain an upper threshold
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parameter (UTC) obtained as CT−1 . The sensitivity of the method is not shown by the signal measured at 1000 rpm.
Engine Percentage of common white pixels Correlation threshold (CT) = 25.08%
Inverted piston 23.49 Overpressure valve 8.80
Exhaust equalizer out of housing 21.64 Oil pump screw improperly tight 21.01
One connecting rod tight with 3 kgm 10.80 Oil jet improperly tight 16.36
Counter-rotating masses with a phase lag 22.87 Rod without a half-bush 21.83
Table 7 - Percentage of common white pixels between faulty and normal pattern engine (120 rpm). 5. CONCLUSIONS The cold test monitoring, i.e. the final test after the assembly line and before shipping the engine to the customer, consists in the final control of engine in order to assess its state of health. The capabilities of the cold test, which are discussed in part I, include monitoring and detection of assembly faults. The test acquisition system collects and processes data through dedicated test algorithms and compares them to fixed thresholds. Techniques based on torque and pressure measurements for cold test applications are already being used widely, whilst in the present work reliable thresholds are obtained by processing the vibration signatures of the engines. In this part of the work the signals are processed using frequency and time-frequency domain techniques. Regarding the study of the Fourier spectra in frequency domain, the area under a spectrum curve is utilized as a parameter that discriminates faulty condition from the normal ones for all the tested faulty engines. Moreover, the mean value of the PSD in the highest amplitude range has the same detection capability. Concerning the time-frequency domain techniques, after the application of the Short Time Fourier Transform the kurtosis coefficient is calculated for both time and frequency distributions: the time kurtosis seems to be sensitive for the faulty discrimination but this statistical parameter does not seem to give an acceptable stability for all the considered faulty conditions tested.. Moreover, the ratio of RMS of the DWT coefficients at the first level of decomposition relative to the signals measured in faulty and the normal conditions is calculated for both investigations at 120 rpm. From the results, this parameter can be considered as a reliable monitoring feature. An image matching correlation of symmetrised dot pattern results is described as well. The SDP visualises vibration signals in a diagrammatic representation in order to quickly detect the faulty engines in cold tests. The percentage of common white pixels between two SDP images (normal and faulty conditions) is another reliable option for monitoring purposes. By observing the results it is difficult to establish an unique operational speed sensitive to all presented methodologies: the speed suitability depends on the different methods applied. The effectiveness and reliability of these techniques are discussed and the influence of the test speed is investigated. The test results show which signal processing technique exhibits higher sensitivity to fault detection at the different operational speeds of the engine. The experimental results indicate that the frequency analysis and image correlation are good efficient solution that can be used in the cold test technology in order to increase its efficiency and fault detection capability. In conclusion, the main original contribution of this work concerns the use of vibration measurements for the quality control of the engine at the end of assembly line, while the greater part of methods used for cold test applications are focused on pressure and torque measurements. The vibration signal is sensitive to all the mentioned faults, while torque, pressure or other measurements are able to detect only some specific faults. Different signal processing tools have been applied to cold test and their monitoring capability has been assessed on the basis of a large number of tests, both in normal and faulty condition.
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ACKNOWLEDGEMENTS The authors wish to thank Apicom and VM Motori (Cento, Ferrara, Italy) and the engineers of these Companies for co-operation and assistance in the collection of engine data. Part of this project has been carried out within the“Laboratorio di Acustica e Vibrazioni” which is supported by Regione Emilia Romagna - Assessorato Attività Produttive, Sviluppo Economico,Piano telematico - Fondi Obiettivo 2 (I). References [1] Delvecchio, S., Dalpiaz, G., Niculita, O., Mucchi, E., 2007. Condition monitoring in diesel engines
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