Gianluca D’Elia

Advanced Signal Processing Tools for the Vibratory Surveillance of Assembly Faults in Diesel Engine Cold Tests

This paper addresses the use of several signal processing tools for monitoring and diagnosis of assembly faults in diesel engines through the cold test technology. One specific fault is considered here as an example: connecting rod with incorrectly tightened screws. First, the experimental apparatus concerning the vibration tests is introduced. Subsequently, the dynamic analysis of the engine has been carried out in order to calculate the connecting rod forces against the crankpin for predicting the position where mechanical impacts are expected. Then, a vibration signal model for this type of faults is introduced. It deals with the cyclostationary model in which the signal is subdivided into two main parts: deterministic and nondeterministic. Finally, the acceleration signals acquired from the engine block during a cold test cycle at the end of the assembly line are analyzed. For quality control purposes in order to obtain reliable thresholds for the pass/fail decision, a method based on the image correlation of symmetrized dot patterns is proposed. This method visualizes vibration signals in a diagrammatic representation in order to quickly detect the faulty engines in cold tests. Moreover, the fault identification is discussed on the basis of the cyclostationary model of the signals. The first-order cyclostationarity is exploited by the analysis of the time synchronous average (TSA). In addition, the residual signal is evaluated by subtracting the TSA from the raw synchronized signal, and thus, the second-order cyclostationarity analysis is developed by means of the Wigner–Ville distribution (WVD), Wigner–Ville spectrum (WVS), and mean instantaneous power. Moreover, continuous wavelet transform is presented and compared with the WVD and WVS.

Combining blind separation and cyclostationary techniques for monitoring distributed wear in gearbox rolling bearings

This work seeks to study the potential effectiveness of the Blind Signal Extraction (BSE) as a pre-processing tool for the detection of distributed faults in rolling bearings. In the literature, most of the authors focus their attention on the detection of incipient localized defects. In that case, classical techniques (i.e. envelope analysis) are robust in recognizing the presence of the fault and its characteristic frequency. However, when the fault grows, the classical approach fails, due to the change of the fault signature. De facto, in this case the signal does not contain impulses at the fault characteristic frequency, but more complex components with strong non-stationary contents. Moreover, signals acquired from complex machines often contain contributions from several different components as well as noise; thus the fault signature can be hidden in the complex system vibration. Therefore, pre-processing tools are needed in order to extract the bearing signature, from the raw system vibration. In this paper the authors focalize their attention on the application of the BSE in order to extract the bearing signature from the raw vibration of mechanical systems. The effectiveness and sensitivity of BSE is here exploited on the basis of both simulated and real signals. Among different procedures for the BSE computation, the Reduced-Rank Cyclic Regression algorithm (RRCR) is used. Firstly a simulated signal including the effect of gear meshing as well as a localized fault in bearings is introduced in order to tune the parameters of the RRCR. Next, two different real cases are considered, a bearing test-rig as an example of simple machine and a gearbox test-rig as an example of complex machine. In both examples, the bearings were degreased in order to accelerate the wear process. The BSE is compared with the usual pre-processing technique for the analysis of cyclostationary signals, i.e. the extraction of the residual signal. The fault detection is carried out by the computation of the Integrated Cyclic Modulation Spectrum on the extracted signals. The results indicate that the extracted signals via BSE clearly highlight the distributed fault signature, in particular both the appearance of the faults as well as their development are detected, whilst noise still hides fault grow in the residual signals.

Application of Cyclostationary Indicators for the Diagnostics of Distributed Faults in Ball Bearings

This paper deals with the detection of distributed faults in ball bearings. In literature most of the authors focus their attention on the detection of incipient localized defects. In that case classical techniques (i.e. statistical parameters, envelope analysis) are robust in recognizing the presence of the fault and its characteristic frequency. In this paper the authors focalize their attention on bearings affected by distributed faults, due to the progressive growing of surface wear or to low-quality manufacturing process. These faults can not be detected by classical techniques; in fact, in this case the signal does not contain impulses at the fault characteristic frequency, but more complex components with strong non-stationary contents. Distributed faults are here detected by means of advanced tools directly derived from the theory of cyclostationarity. In particular three metrics — namely Integrated Cyclic Coherence (ICC), Integrated Cyclic Modulation Coherence (ICMC) and Indicator of Second-Order Cyclostationarity (ICS2x) — have been calculated in order to condense the information given by the cyclostationary analysis and to help the analyst in detecting the fault in a fast fault diagnosis procedure. These indicators are applied on actual signals captured on a test rig where a degreased bearing running under radial load developed accelerated wear. The results indicated that all the three cyclostationary indicators are able to detect both the appearance of a localized fault and its development in a distributed fault, whilst the usual approach fails as the fault grows.Copyright

On the Diagnostics of Planet Gear Bearings

Bearings play a pivotal role in the rotating machine scenario, due to their ubiquity and importance. A crowd of signal processing procedures have been developed in order to extract information about incipient localised faults in bearings from the measured acceleration signals. In the case of bearings for planetary gear applications, additional complexities are introduced. First, transducers may only be placed on the exterior of the gearbox, usually rather far from bearings. Second, the rotational axes of the planet gears are not fixed, i.e. they move with respect to the gearbox housing and thus to the transducers. As a result, the vibration signature of the planet gear bearings can be altered by the variable transfer path. In this condition, the standard signal processing techniques fail, and the characteristic bearing fault frequencies cannot be determined. On the other hand, global indicators of the bearing health may be used, but they are not able to specify where the fault is located. In this paper, a pre-processing technique is applied to the vibration signals of a planetary gearbox in order to highlight the planet gear bearing signatures. This technique is based on the McFaddens time synchronous averaging method to extract the vibration data relative to each planet. Then, cyclostationary techniques such as the Cyclic Power has be applied to extract the bearing signature.

On the Use of Fourier-Bessel Series Expansion for Gear Diagnostics

This work seeks to study the potential effectiveness of Fourier-Bessel (FB) series expansion for gear fault diagnostics. FB series expansion is a method for cross-terms suppression in Wigner-Ville distribution (WVD). In particular, FB series expansion is used as a signal decomposition technique in order to subdivide the signal into its components before WVD evaluation. The pros and cons of this method are highlighted by the analysis of experimental results. In particular two case studies are taken into account: a fatigue crack at the tooth root and spalls of different sizes. The presented results highlight the conditions in which FB decomposition technique is effective in WVD cross-term suppression, the limitations, as well as the advantages in terms of fault identification.

On the Monitoring and Diagnosis of Assembly Faults in Diesel Engine Cold Tests: A Case Study

This work addresses the use of several signal processing tools as a means for the monitoring and the diagnosis of assembly faults in internal combustion (i.c) engines through the cold test technology. Firstly, an approach based on the use of symmetrized dot patterns for the visual characterization of vibration signatures is proposed. This method is applied in order to obtain reliable thresholds for the pass/fail decision after the cold test. Secondly, the fault identification is discussed on the basis of the cyclostationary modeling of the signals. First of all, first-order cyclostationarity is exploited through the analysis of the Time Synchronous Average (TSA). Subsequently, second-order cyclostationarity is developed by means of the mean instantaneous power, Wigner-Ville Distribution (WVD) and Wigner-Ville Spectrum (WVS). Finally, the Continuous Wavelet Transform (CWT) is presented and compared with the Wigner Ville Distribution. In order to show the effectiveness and the limitations of the above-mentioned techniques, tests were carried out for a number of different faults. In this paper the results relative to a specific fault are shown as example.Copyright

On the use of Vibration Signal Analysis for Industrial Quality Control: Part I

Vibration signals can be successfully captured and analyzed for quality control at the end of the production line. Various signal processing techniques and their applications are presented in this paper. These applications demonstrate the importance of selecting proper signal processing tools in order to extract the most reliable information from the signals. The presented applications regards tooth fault detection in helical gears and the detection of assembly faults in diesel engines by means of cold test technology.

Incoming Stall Identification in Axial Compressors by Vibration Analysis

This work addresses on a complete vibro-acoustic characterization of an axial compressor with the aim to foresee the rotor instability. The tests were performed on a turboshaft Allison 250-C18. The compressor is composed of six axial stages and one centrifugal stage. Four vibration signals were simultaneously measured by means of accelerometers, while the acoustic signals were measured by means of two microphones. Two different kinds of tests have been carried out on the compressor that operates at constant speed: in the course of the first test the six signals were acquired at different positions of the throttle opening, whereas during the second test, the signals were acquired while the throttle was gradually opened. The test results show a sensitive increase of the sub-synchronous activity in the accelerometers spectrum map, moreover, closing the throttle, the amplitude of the spectrum components increases. These phenomena can be related to the rotating stall behavior.Copyright

Detection of Generalized Roughness on Ball Bearing by Cyclostationarity Technique

This paper deals with the detection of generalized roughness on ball bearing. In literature most of the authors focus on the detection of incipient single defects. In the early stage of the damage the vibration signal of the bearing contains characteristics components in the frequency spectrum that reveal both the presence of the fault, and on which element of the bearing the damage is placed. In time the damage develops and important parts of the bearing surface (usually the external or the internal rings) become faulted, and the bearing is said to be affected by generalized roughness. In that case the vibration level of the bearing increases and the characteristic frequencies of the damage are no longer recognizable. In this paper the authors propose the use of cyclostationarity to analyse the vibration signal in the generalized roughness condition. The cyclostationarity allows to detect phenomena which happens repeatedly in a signal. In particular the second-order cyclostationarity has been proved to be suitable for bearing diagnostics, since the periodic impact between a ball and a faulted ring happens with a variable period due to slippery between the bearing elements. So far the cyclo-stationarity has been used to detect single defects, in this paper its use is extended to distributed defects. Experimental results are reported. In particular a degreased bearing is placed on a test-rig and radially loaded to accelerate wear phenomena. The vibration signal is then analysed with two techniques compared to each other: the envelope analysis (which is a well-known method taken from the literature) and cyclostationarity.Copyright