Signal Processing Diagnostic Tool for Rolling Element Bearings Using EMD and MED

The signal processing techniques developed for the diagnostics of mechanical components operating in stationary conditions are often not applicable or are affected by a loss of effectiveness when applied to signals measured in transient conditions. In this chapter, an original signal processing tool is developed exploiting some data-adaptive techniques such as Empirical Mode Decomposition, Minimum Entropy Deconvolution and the analytical approach of the Hilbert transform. The tool has been developed to detect localized faults on bearings of traction systems of high speed trains and it is more effective to detect a fault in non-stationary conditions than signal processing tools based on envelope analysis or spectral kurtosis, which represent until now the landmark for bearings diagnostics.

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.

FRC erection stages dynamic behaviour under wind loads

Buffeting response of a cable-stayed bridge under construction is investigated through wind tunnel tests and numerical simulations. Two configurations of the erection stage have been considered and compared in terms of dynamic response and internal forces using the results of the experimental aeroelastic models. Moreover the results of a numerical model able to simulate the simultaneous effects of vortex shedding from tower and aeroelastic response of the deck are compared to the wind tunnel ones.

Developing Co-operating Legal Knowledge Based Systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work supplements rule-based reasoning with case based reasoning and intelligent information retrieval. This research, specifies an approach to the case based retrieval problem which relies heavily on an extended object-oriented / rule-based system architecture that is supplemented with causal background information. Machine learning techniques and a distributed agent architecture are used to help simulate the reasoning process of lawyers. In this paper, we outline our implementation of the hybrid IKBALS II Rule Based Reasoning / Case Based Reasoning system. It makes extensive use of an automated case representation editor and background information.

Mortality following hip arthroplasty—inappropriate use of National Joint Registry (NJR) data

Mortality following hip arthroplasty is affected by a large number of confounding variables each of which must be considered to enable valid interpretation. Relevant variables available from the 2011 NJR data set were included in the Cox model. Mortality rates in hip arthroplasty patients were lower than in the age-matched population across all hip types. Age at surgery, ASA grade, diagnosis, gender, provider type, hip type and lead surgeon grade all had a significant effect on mortality. Schemper's statistic showed that only 18.98% of the variation in mortality was explained by the variables available in the NJR data set. It is inappropriate to use NJR data to study an outcome affected by a multitude of confounding variables when these cannot be adequately accounted for in the available data set.

Unbalance identification in large steam turbo-generator unit using a model-based method

Fault identification in industrial machine is a topic of major importance under engineering point of view. In fact, the possibility to identify not only the type, but also the severity and the position of a fault occurred along a shaft-line allows quick maintenance and shorten the downtime. This is really important in the power generation industry where the units are often of several tenths of meters long and where the rotors are enclosed by heavy and pressure-sealed casings. In this paper, an industrial experimental case is presented related to the identification of the unbalance on a large size steam turbine of about 1.3 GW, belonging to a nuclear power plant. The case history is analyzed by considering the vibrations measured by the condition monitoring system of the unit. A model-based method in the frequency domain, developed by the authors, is introduced in detail and it is then used to identify the position of the fault and its severity along the shaft-line. The complete model of the unit (rotor – modeled by means of finite elements, bearings – modeled by linearized damping and stiffness coefficients and foundation – modeled by means of pedestals) is analyzed and discussed before being used for the fault identification. The assessment of the actual fault was done by inspection during a scheduled maintenance and excellent correspondence was found with the identified one by means of authors’ proposed method. Finally a complete discussion is presented about the effectiveness of the method, even in presence of a not fine tuned machine model and considering only few measuring planes for the machine vibration.

Experimental evidences in bearing diagnostics for traction system of high speed trains

Rolling element bearings are the most critical components in the traction system of high speed trains. Monitoring their integrity is a fundamental operation in order to avoid catastrophic failures and to implement effective condition based maintenance strategies. Generally, diagnostics of rolling element bearings is usually performed by analyzing vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. Several papers have been published on this subject in the last two decades, mainly devoted to the development and assessment of signal processing techniques for diagnostics. The experimental validation of such techniques has been traditionally performed by means of laboratory tests on artificially damaged bearings, while their actual effectiveness in specific industrial applications, particularly in rail industry, remains scarcely investigated. This paper is aimed at filling this knowledge gap, by addressing the diagnostics of bearings taken from the service after a long term operation on the traction system of a high speed train. Moreover, in order to test the effectiveness of the diagnostic procedures in the environmental conditions peculiar to the rail application, a specific test-rig has been built, consisting of a complete full-scale train traction system, able to reproduce the effects of wheeltrack interaction and bogie-wheelset dynamics. The results of the experimental campaign show that suitable signal processing techniques are able to diagnose bearing failures even in this harsh and noisy application. Moreover, the most suitable location of the sensors on the traction system is proposed, in order to limit their number.

Dragline swing automation

Draglines are very large machines that are used to remove overburden in open-cut coal mines. This paper outlines the design of a computer control system to implement an automated swing cycle on a production dragline. Subsystems and sensors have been developed to satisfy the constraints imposed by the task, the harsh operating environment and the mine's production requirements.

Failure to launch: The self-regulating Md -MYB10R6 gene from apple is active in flowers but not leaves of Petunia

Coloured foliage due to anthocyanin pigments (bronze/red/black) is an attractive trait that is often lacking in many bedding, ornamental and horticultural plants. Apples (Malus × domestica) containing an allelic variant of the anthocyanin regulator, Md-MYB10R6, are highly pigmented throughout the plant, due to autoregulation by MYB10 upon its own promoter. We investigated whether Md-MYB10R6 from apple is capable of functioning within the heterologous host Petunia hybrida to generate plants with novel pigmentation patterns. The Md-MYB10R6 transgene (MYB10–R6pro:MYB10:MYB10term) activated anthocyanin synthesis when transiently expressed in Antirrhinumroseadorsea petals and petunia leaf discs. Stable transgenic petunias containing Md-MYB10R6 lacked foliar pigmentation but had coloured flowers, complementing the an2 phenotype of ‘Mitchell’ petunia. The absence of foliar pigmentation was due to the failure of the Md-MYB10R6 gene to self-activate in vegetative tissues, suggesting that additional protein partners are required for Md-MYB10 to activate target genes in this heterologous system. In petunia flowers, where endogenous components including MYB-bHLH-WDR (MBW) proteins were present, expression of the Md-MYB10R6 promoter was initiated, allowing auto-regulation to occur and activating anthocyanin production. Md-MYB10 is capable of operating within the petunia MBW gene regulation network that controls the expression of the anthocyanin biosynthesis genes, AN1 (bHLH) and MYBx (R3-MYB repressor) in petals.

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.

In situ reaction furnace for real-time XRD studies

The new furnace at the Materials Characterization by X-ray Diffraction beamline at Elettra has been designed for powder diffraction measurements at high temperature (up to 1373 K at the present state). Around the measurement region the geometry of the radiative heating element assures a negligible temperature gradient along the capillary and can accommodate either powder samples in capillary or small flat samples. A double capillary holder allows flow-through of gas in the inner sample capillary while the outer one serves as the reaction chamber. The furnace is coupled to a translating curved imaging-plate detector, allowing the collection of diffraction patterns up to 2[theta] [asymptotically equal to] 130°.

Comparison of the sensitivity of different sensor technologies to imbalance severity in low speed wind turbines

Imbalance is not only a direct major cause of downtime in wind turbines, but also accelerates the degradation of neighbouring and downstream components (e.g. main bearing, generator). Along with detection, the imbalance quantification is also essential as some residual imbalance always exist even in a healthy turbine. Three different commonly used sensor technologies (vibration, acoustic emission and electrical measurements) are investigated in this work to verify their sensitivity to different imbalance grades. This study is based on data obtained by experimental tests performed on a small scale wind turbine drive train test-rig for different shaft speeds and imbalance levels. According to the analysis results, electrical measurements seem to be the most suitable for tracking the development of imbalance.