Problem Of Local Damage Detection Research Articles

Assessment of background noise properties in time and time–frequency domains in the context of vibration-based local damage detection in real environment

Any measurement in condition monitoring applications is associated with disturbing noise. Till now, most of the diagnostic procedures have assumed the Gaussian distribution for the noise. This paper shares a novel perspective to the problem of local damage detection. The acquired vector of observations is considered as an additive mixture of signal of interest (SOI) and noise with strongly non-Gaussian, heavy-tailed properties, that masks the SOI. The distribution properties of the background noise influence the selection of tools used for the signal analysis, particularly for local damage detection. Thus, it is extremely important to recognize and identify possible non-Gaussian behavior of the noise. The problem considered here is more general than the classical goodness-of-fit testing. The paper highlights the important role of variance, as most of the methods for signal analysis are based on the assumption of the finite-variance distribution of the underlying signal. The finite variance assumption is crucial but implicit to most indicators used in condition monitoring (such as the root-mean-square value, the power spectral density, the kurtosis, the spectral correlation, etc.), in view that infinite variance implies moments higher than 2 are also infinite. The problem is demonstrated based on three popular types of non-Gaussian distributions observed for real vibration signals. We demonstrate how the properties of noise distribution in the time domain may change by its transformations to the time–frequency domain (spectrogram). Additionally, we propose a procedure to check the presence of the infinite-variance of the background noise. Our investigations are illustrated using simulation studies and real vibration signals from various machines.

Separation of multiple local-damage-related components from vibration data using Nonnegative Matrix Factorization and multichannel data fusion

The problem of local damage detection in case of analyzing vibration signal from rotating machines is mostly related to the detection of periodic impulsive components. Depending on various features of the signal, this task can be relatively simple in some cases (e.g. for an impulsive component in the presence of Gaussian noise). However, in our case multiple impulsive components occupying with the overlapping frequency bands are present. For all components, the impulses can be periodic. In this article, authors present a novel methodology based on data fusion from multichannel vibration data from heavy-duty industrial gearbox operating in the driving station of a belt conveyor. The proposed method is based on the factorization of spectrograms using Generalized Hierarchical Alternating Least Squares Nonnegative Matrix Factorization with Beta-Divergence (later referred to as β-HALS NMF). Partial information obtained from the factorization is fused into a single data set for each impulsive component present in the signal. Finally, Griffin-Lim algorithm is used to estimate the complex phase layer of artificial spectrograms allowing to recover the near-perfect time series of each impulsive component extracted from the signal. This method has been tested on four-channel vibration data.

Novel method of informative frequency band selection for vibration signal using Nonnegative Matrix Factorization of spectrogram matrix

• Novel technique is proposed to diagnose vibration data from rolling bearing. • It allows to isolate component of local damage from among other impulsive components. • Algorithm is based on nonnegative matrix factorization of the spectrogram matrix. • Results are based on vibration data measured on a bearing of a copper ore crusher. The problem of local damage detection in rotating machines is currently the highly important subject of interest in the field of condition monitoring. In the literature one can find many different strategies. One of the most common approaches is the vibration signal analysis aiming at informative frequency band selection. In case of simply structured signals classic methods (e.g. spectral kurtosis) are sufficient and return clear information about the damage. However, in real-world cases the signal is usually much more complicated. Indeed, such signals consist of many different components, for instance: damage-related cyclic impulses, heavy-tailed background noise etc. Hence, there is a growing need for robust damage detection methods. In this paper a novel method of informative frequency band selection is proposed. It utilizes the approach of Non-negative Matrix Factorization applied to time-frequency signal representation. The described algorithm is evaluated using simulated signal containing several different components, that resembles real-life vibration signal from copper ore crusher, as well as real-life signal measured on the crusher. Using the obtained structure of informative frequency band it is possible to filter particular components out of the original signal.

Informative frequency band identification method using bi-frequency map clustering for fault detection in rotating machines

In presented work the problem of local damage detection in rolling element bearings is addressed. Usually such issues require the usage of the techniques of decomposition, separation etc. In such real industrial cases main difficulty lies in relatively low signal-to-noise ratio as well as unpredictable distribution of damage-related information in the frequency domain, hence the typical methods cannot be used. In this paper such industrial scenario is addressed and a simple yet effective approach to underlying component extraction will be discussed. Proposed method analyzes Cyclic Spectral Coherence map as starting data representation, and Expectation-Maximization is used as analytical tool to determine the informative frequency band (IFB) for impulsive component localization in the carrier frequency spectrum. Finally, based on identified IFB, the bandpass filter is constructed to extract the impulsive component from the input signal.

Application of cointegration to vibration signal for local damage detection in gearboxes

The problem of local damage detection for rotating machines is widely studied in the literature. In order to extract the information of the damage, the approach based on the vibration signal analysis is usually applied. Very often classical methods suited for impulsiveness analysis are not sufficient, therefore it is proposed to analyse the signal in terms of its periodicity features. In this paper the cointegration approach is applied to vibration signal in the context of local damage detection in gearbox. The goal of this approach is to recognize if the examined signal comes from healthy or damaged machine. Firstly, we assume periodic correlation of given signal and measure its period. Afterwards, signal is restructured and divided into sub-signals according to the discovered period. Finally, we check if sub-signals are integrated and calculate the cointegrating vector by using the least squares method. In the paper we present details of the method and benefits of using our procedure. To validate our methodology we used simulated vibration signal based on typical gearbox used in mining industry, as well as real data from the gearbox. In the paper authors present a concept of the complete procedure that uses cointegration interpretation as a diagnostic measure.

Nonnegative factorization of spectrogram for local damage detection of belt conveyor gearboxes

The problem of local damage detection for rotating machines is widely studied in the literature. In order to extract information of the damage, the approach based on the vibration signal analysis is applied very often. However, in many cases the analysis of the signal in the time domain is not sufficient. A good choice is the analysis of the signal into the time-frequency domain and extraction of the information on damage from this representation. In this paper, we propose to separate the time-frequency map (spectrogram) of the vibration signal into new partial spectrograms. We assume that one of the new time-frequency representation carries information about the damage, i.e. cyclic impulsive behavior. To perform the separation we apply the idea that is based on a very important topic in numerical algebra, which is matrix factorization. In the presented approach, Convex Non-negative Matrix Factorization (Convex NMF) is used. In the experiments, we analyze a real vibration signal from the heavy duty gearbox used in mining industry. The results obtained for real signal are compared with those obtained using the Spectral Kurtosis approach, one of the classical method used in the problem of local damage detection.

Cyclic sources extraction from complex multiple-component vibration signal via periodically time varying filter

AbstractThe problem of local damage detection is widely discussed in the literature. There are many methods which can be applied, however there is still a need for new techniques addressing specific diagnostic issues. In particular, the case of complex multiple-component vibration signal is a challenging problem. In this paper we focus on such a problem related to a gearbox operating in industrial conditions. Our method consists of several stages. First we transform signal to time-frequency domain using spectrogram. Then for each frequency bin we apply a novel procedure which indicates location of cyclic impulses in given time series. This algorithm is based on the periodically distributed local maxima detection and quantification of their significance. Such procedure requires a priori known fault frequency. If the machine might reveal multiple fault, the procedure has to be calculated separately for each fault frequency. A time-varying filter is designed using the indicated local maxima comprised in the score matrix. Then, signals representing each fault frequency are obtained using inverse short-time Fourier transform algorithm. The method is illustrated with application to simulated and real data from complex mining machine - heavy duty gearbox.

Impulsive Noise Cancellation Method for Copper Ore Crusher Vibration Signals Enhancement

In this paper, we deal with a problem of local damage detection in bearings in the presence of a high-energy impulsive noise. Such a problem was identified during diagnostics of bearings in raw materials crusher. Unfortunately, classical approaches cannot be applied due to the impulsive character of the noise. In this paper we propose, a procedure that cancels out impulsive noise rather than extracts signal of interest. The methodology is based on the regime switching model with two regimes: first corresponding to high-energy noncyclic impulses and second to the rest of the signal. We apply the proposed technique to a simulated signal as well as to the real one. Effectiveness of the method is presented graphically using time series, time-frequency spectrogram, and classical envelope analysis. The obtained results indicate efficiency of the method in impulsive noise cancellation and improve the ability to detect a damage.

Recent Developments in Vibration Based Diagnostics of Gear and Bearings Used in Belt Conveyors

Local damage detection in bearings/gearboxes is one of the most intensively explored problems in condition monitoring literature. Also for mechanical systems used in mining industry this issue might be critical due to short time local overloading of surfaces in contact in gear-pair or bearings that often happens during operation. In general, the problem of local damage detection is well defined in literature, however, specific factors related to the mining industry, require adaptation of existing methods or even developing new approaches. In the paper, some of the most promising techniques with mining machinery context are briefly re-called. The key problems identified for mining machines are: operation under time-varying load/speed conditions, presence of time varying signal to noise ratio and non-Gaussian noise (impulses that appear incidentally, randomly, not with expected cycle or cyclically, however with different cycle related to another damage). All these situations motivated us to find novel solution. The paper might be considered as brief review of recent achievements in the field rather that comprehensive, holistic description of the problem.