Walter Bartelmus

Gearbox Condition Estimation Using Cyclo-Stationary Properties of Vibration Signal

The paper explores the cyclo-stationary properties of vibration signals for estimation of gearbox condition. The advantage of such approach may be clearly seen especially for so called multi-faults problem, i.e. for more than one faults that occurred in the system. In complex mechanical systems like multistage gearboxes, such situation may be often seen. Although this approach becomes more and more popular, it has been noticed that there is difficult to find examples highlighting its potential, especially for real industrial situations. In order to fill partially the gap, the paper deals with the multi fault detection in complex mechanical systems like multi-stage gearboxes: fixed axis and planetary. It has been discussed that during the operation in such machines many faults may appear simultaneously and the classical method like envelope analysis is difficult to use. The paper presents the use of cyclo-stationary properties of signals to identify and characterize sources of modulation. From Spectral Correlation Density Map or more precisely Spectral Coherence Map have been observed the number of sources with different properties of modulation. It is shown that the number of harmonics is important for a kind of fault extraction and interpretation. This approach has been applied to two, three and five stage gearboxes used in mining industry. Vibration signals received in industrial environment during normal operation of objects are considered. It has been also proposed the simple diagnostic feature to estimate the changes of condition with application to a planetary stage in a 5-stage gearbox.

Application of Adaptive Filtering for Weak Impulsive Signal Recovery for Bearings Local Damage Detection in Complex Mining Mechanical Systems Working under Condition of Varying Load

The paper shows application of an adaptive filter as a pre-processor for impulsive cyclic weak signal recovery from raw vibration signals captured from complex mechanical systems used in the industry (namely bearings used in pulleys – parts of driving units for belt conveyors). Periodic/cyclic impulses are related to local faults which cause impulse/concentric forces/stresses in kinematic pairs. Typical examples of such local faults which cause mechanical system condition change are spall/pitting on bearings elements: outer/inner races and/or rolling elements. For analyzed objects, impulses associated with local faults are masked by other signal sources. In the first part of the paper are presented objects for the better understanding of mechanical phenomena that exist in the system, then preliminary signal analysis will be performed (in time, frequency and time-frequency domain) for the identification of signal nature. Next the idea of an adaptive system and the brief description of Normalized Least Mean Square (NLMS) algorithm will be presented. Application of NLMS is better than classical LMS due to stability of the adaptation. In the last section the results of adaptive filtering for signals from bearings is discussed. Authors show application of NLMS (for the first time in literature) for the case when signals are received from machines working in industrial condition. There were made only trails when the machines were investigated in laboratory conditions.

Gearbox damage process

The paper summarises the consideration which is based on the former publications presented in MSSP, European Journal of Mechanics - A/Solids, Key Engineering Materials and others published by the author together with his colleagues. Based on the publication, the conclusion is drawn that the gearboxes may be classified as compound or complex and they consist of one or more stages. The stages of complex gearboxes should be properly separated and later the condition of the stages evaluated using the suitable way of condition monitoring and diagnostic inferring. The stages are treated as a separate unit, which consists of gears, bearings and supported structure. For an each gear stage evaluated, its characteristics are a relation of the diagnostic feature as the function of an external load. It can also be called the susceptibility characteristic to the external load.

STFT Based Approach for Ball Bearing Fault Detection in a Varying Speed Motor

This paper focuses on the diagnostics of ball bearings in direct-drive motors. These specific AC brushless motors are increasing their importance in automation machineries because they can work with a built-in flexibility. In particular the angular displacement of the shaft is continuously monitored by an embedded encoder while the control system allows to perform complex motion profiles such as polynomial ones, even with the inversion of the rotating direction. Direct-drive motors avoid the presence of a mechanical cams or gearboxes between the motor and the load with a subsequent money-saving. On the other side, unfortunately, the diagnostics of ball bearing in those motors is not trivial. In fact most of the solutions proposed in the literature require a constant frequency rotation of the shaft since the characteristic fault frequencies are directly proportional to speed of the motor. It follows that in a varying speed application the fault characteristic frequencies change instantaneously as the rotational frequency does. In this paper an industrial application is considered, where the direct drive motors are used in the kinematic chain of an automated packaging machine performing a cyclic polynomial profile. The basic idea is to focus on signal segmentation using the position profile of the shaft – directly measured by the encoder – as trigger. Next the single cycles of the machine is analysed in time domain, again using encoder signal machine contribution is deleted. Feature extraction for damage detection is done by applying the Short Time Fourier Transform (STFT), the STFT for each cycle is averaged in time-frequency domain in order to enhance fault signature. Finally, the sum of STFT coefficients is used as a simple indicators of damage.

Condition Monitoring of Machinery in Non-Stationary Operations

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Diagnostic information on gearbox condition for mechatronic systems

To maintain mechatronic systems, operational current diagnostic information on gearing condition is needed. Diagnostic information on gearbox condition can be gained from vibration signals. To extract suitable information from the signal we need to know the relation between factors influencing the diagnostic signal and the gearbox condition. There are several factors that have influence on diagnostic signals and these may be divided into four groups: design (DF), production technology (PTF) operational (OF) and change of condition (CCF). To gain information from the vibration signal, many experiments have to be carried out. Because many factors influence the vibration signal, the experiments on real objects are very expensive. To reduce the cost, mathematical modelling and computer simulation may be used. Papers devoted to mathematical modelling and computer simulation are concentrated on one-stage gearboxes. In this research work, a system that consists of a driving motor, coupling, double-stage gearbox and driven machine is considered. Using the model, it is possible to investigate the above-mentioned factors that influence the vibration signal. Faults occur during a gearbox operation. Gearing faults may be divided into single faults (a crack in a tooth or its breakage) and distributed faults caused by gearing wear (pitting, scuffing, abrasion and erosion). The methods of fault detection using signal analysis is given in this paper. The paper shows how mathematical modelling and computer simulations help signal interpretation, taken from real measurements. Frequency, time and time-frequency analysis are used for signal interpretation. As a result, we obtain spectra, cepstra and time-frequency spectrograms. The paper refers to the condition monitoring method, which used simple wide-band signal estimators. The method may be easily adopted for use in monitoring the online gearbox condition in mechatronics systems, whereas an advanced signal interpretation may be used for offline condition monitoring.

Computerised Decision-Making Support System Based on Data Fusion for Machinery System’s Management and Maintenance

In the present fast-paced world of business in a highly competitive marketplace, the focus on increasing the efficiency of business processes seems to be a reasonable challenge. For this reason in order to optimize production costs, there is an increasing trend toward maximising the use of the operational capabilities of the equipment – technological line components – with the prevention of failure events and their consequences in the form of costly repairs or replacements. A condition-based maintenance (CBM) approach allows to globally monitor, maintain and control operation of the whole complex of equipment and/or processes. The CBM effectively supports decision-making process concerning their further use or determination of the optimum repair/replacement schedule. The presented decision support system is dedicated for an underground copper mine, where the network of belt conveyors is a critical part of the production process. Due to complexity of mechanical system, harsh mining environment and presence of many degradation factors, development of the effective CBM system seems to be justified. It requires the integration of data from different sources, adaptation of advanced data mining techniques, procedures or various diagnostic methods. Because of the multidimensional nature of diagnostic data and diversified technical configurations of the facilities, it was necessary to develop and implement multivariate analytical models based on artificial intelligence techniques. Consequently, it allows to achieve improvement of efficiency of transportation network and reduction of repairs costs and unplanned breakdowns. In this paper we will briefly refer to all these issues.

New Focus on Gearbox Condition Monitoring for Failure Prevention Technology

Condition monitoring is a tool for detection of faults and failure prevention. Fault andfailure are regarded as inevitable during the machine operation as the process of wear and theprocess of degradation. The question is, if one can influence the wear and degradation process,using condition monitoring. The paper will present technology which demonstrates that the use ofthe proper method can influence the wear and machine degradation process, using proper conditionmonitoring techniques and knowing scenarios of wear and degradation process. In the discussionpresented in the paper as a prerequisite has been taken that machinery works in severe dustyenvironment and varying operation conditions. It has been pointed that degradation process is notjust simply development of one fault. Most research for developing technology for conditionmonitoring is concentrated on one fault development. If one considers condition monitoring for acrack and brakeage of a tooth in gearbox, one should take in consideration that tooth crack andbrakeage is the result of several events, like rolling elements bearing frictional wear, which causesecondary misalignment of shaft and gears. The frictional wear is caused by dust particles whichget into oil from the environment in which a gearbox is operating. To avoid an influence ofcontaminated oil, contamination proactive technology should be used for the assessment of thedegree of contamination and the decision on oil purification or change should be taken. The wholeprocess connected with a gearbox condition change (wear and degradation process) shall bedescribed in the paper. The oil purification or its replacement extends the live of gearboxes butlong live of a gearbox, even with very little contamination causes some frictional wear of bearingsand finally secondary misalignment. To avoid further development of degradation process propertechnology should be used. There is a need to measure the degree of misalignment and makedecision on bearings replacement, in order to avoid further gearbox degradation, like teeth scuffingwhich may leads to crack initiation.

Some Remarks on Using Condition Monitoring for Spatially Distributed Mechanical System Belt Conveyor Network in Underground Mine – A Case Study

The paper deals with application of condition monitoring and information system to maintain of complex, spatially distributed machinery system, namely belt conveyor transportation network, which consists of hundreds of drive units located on mine territory. There is simple question: what managers/engineers should do to ensure safe and efficient work of transportation machines? It has appeared that number of objects, their spatial location, specific structure of mining company, harsh environment, diversity of machines etc make this problem really complicated. It is obvious that there is a need to use specialized equipment, software but first of all set of procedures of data acquisition, validation, processing, storage, visualization, decision making, reporting etc, so in other words maintenance management. All these stages, combined and implemented as maintenance management software called Diag Manager (CMMS class) is discussed here.

Novel Techniques of Diagnostic Data Processing for Belt Conveyor Maintenance

In the paper a new diagnostic approach for gearbox used in belt conveyors will be discussed. The purpose of the work is to provide novel view on diagnostic data processing in the context of detection of changes in condition for population of gearboxes used in belt conveyor network. The idea will be presented by examples: a data base of diagnostic features collected during last 3 years (real data from conveyors operating in mining company) will be used for illustration.