A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions

In the field of rolling element bearing diagnostics envelope analysis, and in particular the squared envelope spectrum, have 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 squared envelope spectrum has been extended to cases in which small speed fluctuations occur, maintaining the effectiveness and efficiency that characterize this successful technique. However, the constraint on speed has to be removed completely, making envelope analysis suitable also for speed and load transients, to implement an algorithm valid for all the industrial application. 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 paper is aimed at providing and testing 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.

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.

Large-scale analysis of formations in soccer

Due to the demand for better and deeper analysis in sports, organizations (both professional teams and broadcasters) are looking to use spatiotemporal data in the form of player tracking information to obtain an advantage over their competitors. However, due to the large volume of data, its unstructured nature, and lack of associated team activity labels (e.g. strategic/tactical), effective and efficient strategies to deal with such data have yet to be deployed. A bottleneck restricting such solutions is the lack of a suitable representation (i.e. ordering of players) which is immune to the potentially infinite number of possible permutations of player orderings, in addition to the high dimensionality of temporal signal (e.g. a game of soccer last for 90 mins). Leveraging a recent method which utilizes a "role-representation", as well as a feature reduction strategy that uses a spatiotemporal bilinear basis model to form a compact spatiotemporal representation. Using this representation, we find the most likely formation patterns of a team associated with match events across nearly 14 hours of continuous player and ball tracking data in soccer. Additionally, we show that we can accurately segment a match into distinct game phases and detect highlights. (i.e. shots, corners, free-kicks, etc) completely automatically using a decision-tree formulation.

Impacts of different wind power generators on power system small signal and transient stability

With the ever-increasing penetration level of wind power, the impacts of wind power on the power system are becoming more and more significant. Hence, it is necessary to systematically examine its impacts on the small signal stability and transient stability in order to find out countermeasures. As such, a comprehensive study is carried out to compare the dynamic performances of power system respectively with three widely-used power generators. First, the dynamic models are described for three types of wind power generators, i. e. the squirrel cage induction generator (SCIG), doubly fed induction generator (DFIG) and permanent magnet generator (PMG). Then, the impacts of these wind power generators on the small signal stability and transient stability are compared with that of a substituted synchronous generator (SG) in the WSCC three-machine nine-bus system by the eigenvalue analysis and dynamic time-domain simulations. Simulation results show that the impacts of different wind power generators are different under small and large disturbances.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

Condition monitoring and fault diagnosis of diesel engines using instantaneous angular speed analysis

Continuous monitoring of diesel engine performance is critical for early detection of fault developments in an engine before they materialize into a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few nonintrusive condition monitoring techniques that can be utilized for such a task. Furthermore, the technique is more suitable for mass industry deployments than other non-intrusive methods such as vibration and acoustic emission techniques due to the low instrumentation cost, smaller data size and robust signal clarity since IAS is not affected by the engine operation noise and noise from the surrounding environment. A combination of IAS and order analysis was employed in this experimental study and the major order component of the IAS spectrum was used for engine loading estimation and fault diagnosis of a four-stroke four-cylinder diesel engine. It was shown that IAS analysis can provide useful information about engine speed variation caused by changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectra directly associated with the engine firing frequency (at twice the mean shaft rotating speed) can be utilized to estimate engine loading condition regardless of whether the engine is operating at healthy condition or with faults. The amplitude of this order component follows a distinctive exponential curve as the loading condition changes. A mathematical relationship was then established in the paper to estimate the engine power output based on the amplitude of this order component of the IAS spectrum. It was further illustrated that IAS technique can be employed for the detection of a simulated exhaust valve fault in this study.

Genome-wide analysis in a murine Dnmt1 knockdown model identifies epigenetically silenced genes in primary human pituitary tumors

DNA methylation at promoter CpG islands (CGI) is an epigenetic modification associated with inappropriate gene silencing in multiple tumor types. In the absence of a human pituitary tumor cell line, small interfering RNA-mediated knockdown of the maintenance methyltransferase DNA methyltransferase (cytosine 5)-1 (Dnmt1) was used in the murine pituitary adenoma cell line AtT-20. Sustained knockdown induced reexpression of the fully methylated and normally imprinted gene neuronatin (Nnat) in a time-dependent manner. Combined bisulfite restriction analysis (COBRA) revealed that reexpression of Nnat was associated with partial CGI demethylation, which was also observed at the H19 differentially methylated region. Subsequent genome-wide microarray analysis identified 91 genes that were significantly differentially expressed in Dnmt1 knockdown cells (10% false discovery rate). The analysis showed that genes associated with the induction of apoptosis, signal transduction, and developmental processes were significantly overrepresented in this list (P < 0.05). Following validation by reverse transcription-PCR and detection of inappropriate CGI methylation by COBRA, four genes (ICAM1, NNAT, RUNX1, and S100A10) were analyzed in primary human pituitary tumors, each displaying significantly reduced mRNA levels relative to normal pituitary (P < 0.05). For two of these genes, NNAT and S100A10, decreased expression was associated with increased promoter CGI methylation. Induced expression of Nnat in stable transfected AtT-20 cells inhibited cell proliferation. To our knowledge, this is the first report of array-based "epigenetic unmasking" in combination with Dnmt1 knockdown and reveals the potential of this strategy toward identifying genes silenced by epigenetic mechanisms across species boundaries.

Simulation study for analysis of online dielectric measurement problems on transformer insulation system

Determining the condition as well as the remaining life of an insulation system is essential for the reliable operation of large oil-filled power transformers. Frequency-domain spectroscopy (FDS) is one of the diagnostic techniques used to identify the dielectric status of a transformer. Currently, this technique can only be implemented on a de-energized transformer. This paper presents an initial investigation into a novel online monitoring method based on FDS dielectric measurements for transformers. The proposed technique specifically aims to address the real operational constraints of online testing. This is achieved by designing an online testing model extending the basic “extended Debye” linear dielectric model and taking unique noise issues only experienced during online measurements into account via simulations. Approaches to signal denoising and potential problems expected to be encountered during online measurements will also be discussed. Using fixed-frequency sinusoidal excitation waveforms will result in a long measurement times. The use of alternatives such as a chirp has been investigated using simulations. The results presented in the paper predict that reliable measurements should be possible during online testing.

Plasmon-tuned silver colloids for SERRS analysis of methemoglobin with preserved nativity

Optically tuned silver nanoparticles (AgNP's) functionalized with ω-mercaptoalkanoic acids are synthesized and used as a signal amplifier for the surface-enhanced resonance Raman scattering (SERRS) study of heme cofactor in methemoglobin (metHb). Even though both mercaptopropionic acid (MPA)- and mercaptononanoic acid (MNA)-functionalized AgNP's exemplify vastly enhanced SERRS signal of metHb, MNA-AgNP's amplify the SERRS signal amid preservation of the nativity of the heme pocket, unlike MPA-AgNP's. The electrostatic interaction between MNA-AgNP's and metHb leads to instant signal enhancement with a Raman enhancement factor (EF(SERS)) of 4.2 × 10(3). Additionally, a Langmuir adsorption isotherm has been employed for the adsorption of metHb on the MNA-AgNP surface, which provides the real surface coverage and equilibrium constant (K) of metHb as 139 nM and 3.6 × 10(8) M(-1), respectively. The lowest detection limit of 10 nM for metHb has been demonstrated using MNA-AgNP's besides retaining the nativity of the heme pocket.

Functionalized Ag nanoparticles with tunable optical properties for selective protein analysis

We present a preparation procedure for small sized biocompatibly coated Ag nanoparticles with tunable surface plasmon resonances. The conditions were optimised with respect to the resonance Raman signal enhancement of heme proteins and to the preservation of the native protein structure....

Echolocation calls of the long tailed bat: a quantitative analysis of types of calls

The echolocation calls of long-tailed bats (Chalinolobus tuberculatus) were recorded in the Eglinton Valley, Fjordland, New Zealand, and digitized for analysis with the signal-processing software. Univariate and multivariate analyses of measure features facilitated a quantitative classification of the calls. Cluster analysis was used to categorize calls into two groups equating to search and terminal buzz calls described qualitatively for other species. When moving from search to terminal phases, the calls decrease in bandwidth, maximum and minimum frequency of call, and duration. Search calls begin with a steep-downward FM sweep followed by a short, less-modulated component. Buzz calls are FM sweeps. Although not found quantitatively, a broad pre-buzz group of calls also was identified. Ambiguity analysis of calls from the three groups shows that search-phrase calls are well suited to resolving the velocity of targets, and hence, identifying moving targets in a stationary clutter. Pre-buzz and buzz calls are better suited to resolving range, a feature that may aid the bats in capture of evasive prey after it has been identified.

Design of coreless PCB transformers for power and signal isolation in a modular ADC system for power quality data acquisition

Available industrial energy meters offer high accuracy and reliability, but are typically expensive and low-bandwidth, making them poorly suited to multi-sensor data acquisition schemes and power quality analysis. An alternative measurement system is proposed in this paper that is highly modular, extensible and compact. To minimise cost, the device makes use of planar coreless PCB transformers to provide galvanic isolation for both power and data. Samples from multiple acquisition devices may be concentrated by a central processor before integration with existing host control systems. This paper focusses on the practical design and implementation of planar coreless PCB transformers to facilitate the module's isolated power, clock and data signal transfer. Calculations necessary to design coreless PCB transformers, and circuits designed for the transformer's practical application in the measurement module are presented. The designed transformer and each application circuit have been experimentally verified, with test data and conclusions made applicable to coreless PCB transformers in general.