P. Borghesani

Effectiveness of MED for Fault Diagnosis in Roller Bearings

Diagnostics of rolling element bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a single algorithm different signal-enhancement and signal-analysis techniques. Among the first ones, Minimum Entropy Deconvolution (MED) has been pointed out as a key tool able to highlight the effect of a possible damage in one of the bearing components within the vibration signal. This paper presents the application of this technique to signals collected on a simple test-rig, able to test damaged industrial roller bearings in different working conditions. The effectiveness of the technique has been tested, comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race and rollers. Since MED performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This represents an original contribution of the paper.

The Combination of Empirical Mode Decomposition and Minimum Entropy Deconvolution for Roller Bearing Diagnostics in Non-Stationary Operation

Diagnostics is based on the characterization of mechanical system condition and allows early detection of a possible fault. Signal processing is an approach widely used in diagnostics, since it allows directly characterizing the state of the system. Several types of advanced signal processing techniques have been proposed in the last decades and added to more conventional ones. Seldom, these techniques are able to consider non-stationary operations. Diagnostics of roller bearings is not an exception of this framework. In this paper, a new vibration signal processing tool, able to perform roller bearing diagnostics in whatever working condition and noise level, is developed on the basis of two data-adaptive techniques as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED), coupled by means of the mathematics related to the Hilbert transform. The effectiveness of the new signal processing tool is proven by means of experimental data measured in a test-rig that employs high power industrial size components.

Speed-based diagnostics of aerodynamic and mass imbalance in large wind turbines

Any kind of imbalance in the operation of a wind turbine has adverse effect on the downstream torsional components as well as tower structure. It is crucial to detect imbalance at its very inception. The identification of the type of imbalance is also required so that appropriate measures of fault accommodation can be performed in the control system. In particular, it is important to distinguish between mass and aerodynamic imbalance. While the former is gradually caused by a structural anomaly (e.g. ice deposition, moisture accumulation inside blade), the latter is generally associated to a fault in the pitch control system. This paper proposes a technique for the detection and identification of imbalance fault in large scale wind turbines. Unlike most other existing method it requires only the rotor speed signal which is readily available in existing turbines. Signature frequencies have been proposed in this work to identify imbalance type based on their physical phenomenology. The performance of this technique has been evaluated by simulations using an existing benchmark model. The effectiveness of the proposed method has been confirmed by the simulation results.

Optimal Frequency Band Selection for the Square Envelope Spectrum in the Diagnostics of Rolling Element Bearings

Diagnostics of rolling element bearings is usually performed by means of a second-order cyclostationary tool applied to the vibration signal, due to the stochastic nature of the contact between the defect and the bearing rolling elements. The most used and simple method is the Envelope Analysis that is based on the identification of bearing damage frequency components in the so-called Square Envelope Spectrum. The main critical point of this technique is the selection of a suitable frequency band for the demodulation of the vibration signal. The most used approach for the frequency band selection is based on the evaluation of the band-Kurtosis index by mean of diagrams as the frequently used Fast Kurtogram or the more recent Protrugram. Both of them may fail in the selection of the optimal frequency band when other vibration sources affect the Kurtosis index. Also critical is the constancy in the time of this optimal band. In the paper, an experimental case of a bearing damage is investigated and an alternative approach for the filter band selection, the so-called “PeaksMap”, will be proposed by the authors and compared with the ones available in the literature.© 2014 ASME

Wheel slip control based on traction force estimaton of electric locomotives

In the development of control strategies for traction control in railway systems or locomotive applications, the dynamic interaction is known as traction force coefficient between the wheel and the rail must be carefully considered. Identification of the traction force coefficient between a railway wheel and rail track is a challenging task because of the difficulty in its measurement directly. Railway systems experiences wheel slip due to their acceleration and deceleration and also due to rail surfaces. Therefore, the traction force coefficient is an important factor in maintaining high acceleration and braking performance of railway. This study proposes a new approach to estimate the traction force coefficient using a Kalman Filter structured to included unknown input and then utilize the traction force between the wheel and rail where it reaches the maximum traction force level which is an adhesive force. For proper acceleration or braking of the wheel, the driving torque should be kept near to the adhesive level for the desired anti-slip control. Consequently, the motor drive system is required to make an instant and fast torque control to adjust the speed of a railway vehicle. The focus of this research project is to develop a dynamic model of the wheel slip controller that will be subsequently used to design a model for an induction motor driven locomotive.

Diagnostic of rolling element bearings with envelope analysis in non-stationary conditions

In the field of rolling element bearing diagnostics, envelope analysis has gained in the last years a leading role among the different digital signal processing techniques. The original constraint of constant operating speed has been relaxed thanks to the combination of this technique with the computed order tracking, able to resample signals at constant angular increments. In this way, the field of application of this technique has been extended to cases in which small speed fluctuations occur, maintaining high effectiveness and efficiency. In order to make this algorithm suitable to all industrial applications, the constraint on speed has to be removed completely. In fact, in many applications, the coincidence of high bearing loads, and therefore high diagnostic capability, with acceleration-deceleration phases represents a further incentive in this direction. This chapter presents a procedure for the application of envelope analysis to speed transients. The effect of load variation on the proposed technique will be also qualitatively addressed.

Fault Symptoms of Rolling Element Bearings Under Variable Operating Conditions: A Multi Domain Analysis

Diagnostics of rolling element bearings have been traditionally developed for constant operating conditions, and sophisticated techniques, like Spectral Kurtosis or Envelope Analysis, have proven their effectiveness by means of experimental tests, mainly conducted in small-scale laboratory test-rigs. Algorithms have been developed for the digital signal processing of data collected at constant speed and bearing load, with a few exceptions, allowing only small fluctuations of these quantities. Owing to the spreading of condition based maintenance in many industrial fields, in the last years a need for more flexible algorithms emerged, asking for compatibility with highly variable operating conditions, such as acceleration/deceleration transients. This paper analyzes the problems related with significant speed and load variability, discussing in detail the effect that they have on bearing damage symptoms, and propose solutions to adapt existing algorithms to cope with this new challenge. In particular, the paper will i) discuss the implication of variable speed on the applicability of diagnostic techniques, ii) address quantitatively the effects of load on the characteristic frequencies of damaged bearings and iii) finally present a new approach for bearing diagnostics in variable conditions, based on envelope analysis. The research is based on experimental data obtained by using artificially damaged bearings installed on a full scale test-rig, equipped with actual train traction system and reproducing the operation on a real track, including all the environmental noise, owing to track irregularity and electrical disturbances of such a harsh application.

Using support vector machines for the computationally efficient identification of acceptable design parameters in computer-aided engineering applications

SVMs are used to estimate the boundary of acceptable design parameters.An active learning method is developed to efficiently refine the boundary estimate.The algorithm is applied to a (known) toy function to demonstrate its effectiveness.The approach is subsequently used to find the dynamic stability limit of a train. This paper addresses the problem of estimating continuous boundaries between acceptable and unacceptable engineering design parameters in complex engineering applications. In particular, a procedure is proposed to reduce the computational cost of finding and representing the boundary. The proposed methodology combines a low-discrepancy sequence (Sobol) and a support vector machine (SVM) in an active learning procedure able to efficiently and accurately estimate the boundary surface. The paper describes the approach and methodological choices resulting in the desired level of boundary surface refinement and the new algorithm is applied to both two highly-nonlinear test functions and a real-world train stability design problem. It is expected that the new method will provide designers with a tool for the evaluation of the acceptability of designs, particularly for engineering systems whose behaviour can only be determined through complex simulations.

An investigation on factors influencing dust accumulation on CSP mirrors

The profitability of a CSP plant is highly affected by the efficiency of the solar field: it is essential to maintain mirrors’ reflectivity at high level to avoid thermal power loss. Dust fouling is the main cause of reflectivity loss and cleaning of mirrors is a crucial activity to restore economical level of reflectivity. However, the high cost of cleaning operations requires the study and identification of a balanced plan for the dust removal. The dust generation and transport to the plant site is the first mechanism that needs to be modelled to identify the optimal schedule for cleaning operations and it is highly dependent on weather conditions. Several studies have suggested a dependency of reflectors performance with humidity level, frequency of rainfalls, wind and mirrors’ tilting angle, however rarely quantitative correlation studies have been performed to validate these hypotheses. The aim of this research is to provide an in-depth insight on interaction between the main parameters and airborne ...

Architecture of the monitoring system for the traction system bearings of a regional locomotive

Diagnostics of rolling element bearing is generally performed by suitable signal analysis tools for the vibration data. In the railway field this analysis is a complicated tasks due to variable operating conditions of the system in terms of load, speed and temperature. At present, the widely maintenance policy in railway field is based on train mileage. The natural increase of trains in the time for the same regional area is leading to more economical maintenance approaches as the condition-based one, where the maintenance activities are performed only when is economically profitable. The secondary effect of such efficient maintenance approach is the reduction of unexpected stopping failures. Besides, condition-based maintenance requires a suitable monitoring system able to analyze and track the health of the mechanical components. In the paper, the complex architecture of the monitoring system for the rolling element bearing of a regional locomotive will be shown.