Piotr Kruczek

Cyclic modulation spectrum — An online algorithm

Many profitable methods of damage detection in rotating machines involve cyclostationarity. This approach exploits the fact that vibrations of a damaged machinery possess a periodic envelope. The frequency related to this periodic behavior is strictly related to one of the characteristic frequencies associated with design and specific operation of the considered machine. Nowadays, such methods are rarely implemented in industrial condition monitoring systems for several reasons. One of the main reasons is related to computational complexity. Algorithms that calculate cyclostationary features are said to be computationally expensive, thus their involvement in monitoring systems is limited. This drawback becomes critical in online systems in which the monitored features are updated just after arrival of the next measurement. Because of complexity of cyclostationary approach other tools are preferred instead, especially those which do not require intensive computations. In this paper we discuss algorithms for calculation of a basic cyclostationary tool, namely cyclic modulation spectrum (CMS). In this paper we provide several novel algorithms that update CMS when new measurements arrive.

Application of compound Poisson process for modelling of ore flow in a belt conveyor system with cyclic loading

Abstract This paper deals with the analysis of the random process of cyclic loading of a mining belt conveyor with portions of ore discharged by loaders or trucks. Such transfer of transported ore from a cyclic to a continuous transport is typical for the specific mining operations implemented in the underground copper ore mines with room and pillar mining. The conveyors in such systems are usually significantly oversized in order to match the peak loads of cumulated discharges of ore hauled from mining fields by loaders. Therefore, the actual loadings that occur in the mining transportation systems need to be analysed to provide the data for more accurate design and control of belt conveyors. The large data-set of actual loadings of the belt conveyors has been used for the stochastic modelling of the analysed process. The compound Poisson process has been proposed as mathematical tool to analyse/describe properties of ore flow. The discussion of the chosen distribution functions and results of the fitted model simulations compared with the examined measurement data are presented. In this paper, the case of a belt conveyor loaded only by a single feeding point where loaders are randomly discharged have been analysed. More complex cases (several loading points, mixed ore supply from cyclic and continuous ore mass flow from preceding conveyors or ore bunkers) are under investigation and will be presented in the future.

Modified Protrugram Method for Damage Detection in Bearing Operating Under Impulsive Load

The problem of damage detection in rotating machines is related not only to specific speed or load fluctuations. Sometimes the environment of a gear or bearing makes the diagnosis a challenging problem. In this paper a novel signal processing method based on cyclostationary approach is proposed. It is designed in order to indicate and separate source of the signal that could be related to damage of the machine. The vibration signal related to such source can be considered as a cyclic pulse train. Thus, it is worth to benefit not only from impulsivity of the signal but also from its cyclic nature—taking advantage from these two properties in the same time might be more beneficial. We propose a method that indicates frequency bands where the cyclic pulse train occurs. On the other hand, the method is robust to contamination which might be observed in real-world examples, e.g. non-cyclic high-energy impulses or amplitude modulated sine waves. The motivation for such method is derived from a real-world example—a rolling element bearing operating in a hummer crusher. We compare our method with two other well-known methods for damage detection in rotating machines, namely protrugram and spectral kurtosis.

Time-Frequency Identification of Nonlinear Phenomena for Mechanical Systems with Backlash

The paper presents result of non-linearity analysis in the steering gear with various backlash. Due to mechanism of steering gear clearance of gear teeth usually generate non-linearity of movement transfer. Thus, it can be considered as main source of non-linearity in vibration signal. The main goal of the article is to observe how the gear backlash level influences the vibration signal and nonlinear phenomena. The signal was recorded on automobile steering system model with a special modified steering gear. The experiment consists of vibration measurements in 3 orthogonal directions for steering gear without backlash, 0.5 mm of backlash and with 0.9 mm of backlash. The advanced non-stationary signal processing methods for evaluation of steering gear backlash were applied. The presented methodology allows complex observation of the vibration signal, especially when non-linear processes occur in the system. Therefore, energy and dynamics of the signal can be observed. Furthermore, it is possible to analyse amplitudes of time-frequency representation for different gear backlashes, which enables evaluation of non-linearity of the signal.

Vibration Signal Processing for Identification of Relation Between Liquid Volume and Damping Properties in Hydraulic Dampers

The paper presents analysis of vibration transferred on the car body under different damping properties of suspension. Vehicle must be considered as a multi-technical system in which non-linear phenomena occur. The springing elements in modern cars are coil springs with non-linear characteristics, and the damping elements are telescopic shock absorbers with asymmetrical, strongly non-linear damping characteristics. The scope of the research contains relation between liquid volume and damping properties in hydraulic dampers and analysis of vibration of vehicle forced to vibration by an exciter machine. For comprehensive analysis of the vibration signals their representation in time, frequency and joint time-frequency domains are computed. The paper investigates several different liquid volumes, i.e. from 0% to 100%. The methodology is based on advanced signal analysis designed to determine the influence of fluid volume on the damper characteristics.

Fault Detection in Belt Conveyor Drive Unit via Multiple Source Data

The fault detection for the belt conveyor drive unit gives a possibility to avoid unexpected breaks in production and to plan the repairs for the most convenient time. Trouble-free operation of the belt conveyor system in an underground mine is one of the most essential requirement in case of production plan realization. In order to maintain the system, several features (energy consumption, output, temperature, vibrations, belt speed etc.) might be monitored. For instance, belt conveyors can be equipped with sensors, which measure the temperature of their transmission and the drive unit electricity consumption. The paper aims to examine the possibility of multivariate data analysis application for fault detection. The authors focused on the extraction of additional information from the relation between the transmission temperature and electrical energy consumption (electric current). Namely, in the first step, the electric current signal is smoothed in order to reduce its high-frequency volatility. Then the relationship between smoothed current signal and the temperature is examined. It was observed that the specifically smoothed current might explain the variation of the temperature. Variability of fitted model parameters may indicate a change in the drive unit condition. The findings are illustrated by analysis of signals acquired on the machine and it covers period before damage occurred till the repair. Finally, model outputs for the three stages have been analyzed: before the damage, during its development and after repair. The obtained results prove that the relationship between current and temperature depends on the condition of the drive system. The outcome confirms that there is a possibility to apply context-based, multiple sources data analysis for fault detection in belt conveyor drive unit.

Novel method of informative frequency band selection for vibration signal using nonnegative matrix factorization of short-time fourier transform

The problem of local damage detection in rotating machines is currently the highly important subject of interest. 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, high energy non-cyclic impulses not related to damage or 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. Using the obtained structure of informative frequency band it is possible to filter particular components out of the original signal.