Repetitive Impulses Research Articles

Bearing Fault Diagnosis Based on the Maximum Squared-Enveloped Multipoint Kurtosis Morphological Deconvolution

Extracting the fault-related repetitive impulses from the observed signal disturbed by deterministic components, random impulses and background noise are a critical problem in diagnosing the bearing fault. This article proposes a maximum squared-enveloped multipoint kurtosis morphological deconvolution (MSEMKMD) method to tackle this issue. First, an unbiased-autocorrelation double-scale morphological filter (MF) is designed to preserve the repetitive-impulse characteristic of the observed signal and filter out these noises from different sources. The minimum entropy deconvolution (MED) method is then used to enhance recovering the repetitive impulses disturbed by these noises. In this method, the squared-enveloped multipoint kurtosis (SEMK) indicator is developed to select the optimal structural element (SE) scale and filtered result. Finally, the results of analyses with simulation and experimental data show the superiority of the MSEMKMD in fault-related impulse recovery for bearing fault diagnosis.

Adaptive sparse denoising and periodicity weighted spectrum separation for compound bearing fault diagnosis

The compound fault diagnosis of rolling bearings has become a hot topic. In this study, a novel method based on adaptive sparse denoising (ASD) combined with periodicity weighted spectrum separation (PWSS) is proposed to diagnose compound faults in rolling bearings. Specifically, ASD reveals fault types and PWSS separates compound faults. First, ASD determines regularization parameters adaptively using the proposed compound frequency multi D-norm, thereby denoising the raw vibration signal and revealing fault types. Then, PWSS constructs the time-frequency spectrum (TFS) and uses the fault periodicity from ASD to determine the time occurrence positions of the repetitive impulses. With this time occurrence position information, a weight matrix is constructed to reweight the TFS. Finally, through the reweighted TFS, PWSS extracts and separates repetitive impulses from compound faults. The performance of the proposed method is validated in both simulation and experimental studies. The results demonstrate that the proposed method can successfully diagnose and separate the compound faults.

Compound fault diagnosis of rolling bearing using PWK-sparse denoising and periodicity filtering

Since the noise in signals influences the diagnosis and separation of bearing compound faults, this study proposes a new method combining periodicity weighted Kurtosis (PWK)-sparse denoising (SD) with periodicity filtering (PF) to extract repetitive impulses of compound faults. The proposed method involves fault identification and fault separation. Firstly, since Kurtosis only measures transient feature and neglects periodic feature when evaluating repetitive impulses, to overcome this drawback, a new index, PWK, is proposed to adaptively select optimal regularization parameter in SD. Then, with PWK measuring repetitive impulses extracted by SD, PWK-SD is used to identify fault types. Subsequently, based on the fault types, the proposed PF separates the compound bearing faults. During separation processing, PF uses periodic feature of sparse coefficients selecting the relevant impulse atoms to separate mixed repetitive impulses. Simulation and experimental results indicate the effectiveness of the proposed method in diagnosing and separating compound bearing faults.

A novel wideband partial discharge measuring circuit under fast repetitive impulses of static converters

Nowadays fast switching power electronic devices are used for driving high voltage electric rotating machines. These power supplies apply sharp transient voltage at the time of switching. To study the effect of these stresses on the insulation system, investigating Partial Discharge (PD) during voltage rapid changes is of special interest. Although, the implemented coupling capacitor in a conventional PD measuring circuit provides a low impedance pass for the PDs, but imposes a transient current caused by sharp changes of voltage at the time of switching; hence, the measuring impedance of the conventional partial discharge equipment cannot distinguish an actual PD pulse from the mentioned transduced capacitive current pulses. Currently, two options are available in order to cope with this issue, including UHF technique and implementation of band pass filters. On the one hand, calibration of the PD pulses in the UHF method is not possible; hence, the PD test results of different manufacturers of machines under such stresses cannot be compared, which is the main drawback. On the other hand, one has to remove a wide range of frequency components of PD pulses that makes PD calibration procedure to be invalid. In this paper, a resonant full bridge topology is implemented to cancel out the capacitive pulse shaped current, where the PD pulses are captured directly via electrical method in a symmetric introduced configuration. The resulting inrush current has drastically reduced and a wideband measuring circuit with a true calibration process has become possible.

Blind Deconvolution Technique Based on Improved Correlated Generalized Lp/Lq Norm for Extracting Repetitive Transient Feature

Since minimum correlated generalized Lp/Lq deconvolution (MCG-Lp/Lq-D) cannot simultaneously have a good sensing ability to repetitive fault impulses and a strong robustness to noise when processing low SNR signals, a new deconvolution method, minimum improved correlated generalized Lp/Lq deconvolution (MICG-Lp/Lq-D), is proposed in this article. The proposed method is designed based on a novel norm, the improved correlated generalized Lp/Lq (ICG-Lp/Lq) norm. The ICG-Lp/Lq norm not only has the low-order statistics form that is similar to the correlated generalized Lp/Lq (CG-Lp/Lq) norm used in MCG-Lp/Lq-D, but also has the high shift-order signal correlation construction similar to the correlated kurtosis (CK) norm used in maximum correlated kurtosis deconvolution (MCKD). This guarantees that the proposed norm is robust enough to strong noise and can improve its sensing ability for repetitive impulses simultaneously. This feature of the ICG-Lp/Lq norm ensures that MICG-Lp/Lq-D can suppress outliers and extract weak impacts when processing low SNR signals. Simulated and experimental case studies verify the performance of the proposed method. The results indicate that the proposed method is robust enough to strong noise and very sensitive to repetitive impulses simultaneously, which indeed overcomes the aforementioned defects of MCG-Lp/Lq-D. Therefore, MICG-Lp/Lq-D can be considered a promising tool for detecting repetitive impulses.

BARONESS CHARLOTTA CASTELLI GLEMBAY - WAS SHE HYPERSEXUAL?

Hypersexuality disorder (or sexual addiction or excessive sexual drive or compulsive sexual behaviour disorder) is a controversial condition that is present in the International Classification of Disease but not in the Diagnostic and Statistical Manual for Mental Disorders. It is defined as a clinical syndrome characterised by a persistent pattern of failure to control intense, repetitive sexual impulses or urges resulting in repetitive sexual behaviour. The condition is more prevalent in men than in women. Some medical conditions were described in fictional literature before their formal recognition in medicine, like Huntington disease, Pickwick syndrome, and Munchausen syndrome. The aim of this article is to analyse the fictional character of Charlotta Castelli Glembay from Miroslav Krleža’s play Messrs Glembays from 1928. Krleža presented a woman with a sexual drive that could be described as uncontrollable, organic (physical) in origin, and different from love and affection (that she also experienced, but only with one particular man). The author gave a special name for her condition – erotic intelligence. This sexual behaviour has distressing and devastating consequences. This paper will argue that the play depicts hypersexuality disorder in a woman, with a designation of its aetiology. In concordance with the prevailing attitudes of the time (the early 20th century), hypersexuality in women had negative attributions.

Blind deconvolution assisted with periodicity detection techniques and its application to bearing fault feature enhancement

Abstract Maximum correlated kurtosis deconvolution (MCKD), multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and maximum second-order cyclostationarity blind deconvolution (CYCBD) have remarkable performances in extracting periodic impulses. However, these deconvolution methods highly rely on the prior period of measured signal and can only enhance the specific periodic impulses. Aiming at these limitations, six kinds of periodicity detection techniques (PDTs) are introduced to adaptively identify the period of repetitive impulses. Further, PDTs-assisted MCKD, MOMEDA and CYCBD are proposed for bearing fault feature enhancement. The improved deconvolution methods have two characteristics: first, the fault period is automatically identified by PDTs according to the characteristics of the measured signal; second, the impulses of different faults can be enhanced adaptively. The analysis results of simulated and experimental datasets demonstrated the better capability of the proposed methods in enhancing bearing fault features with respect to original deconvolution methods and the fast kurtogram method.

Electromyographic and ultrasonographic evaluation of neuromuscular electrical stimulation training on the equine rectus abdominis muscle

The current study aimed to determine the effects of neuromuscular electrical stimulation (NMES) on equine rectus abdominis using surface electromyography (sEMG) and ultrasonographic muscle thickness evaluation. Five horses were trained with NMES for 12 weeks; muscle thickness and sEMG evaluations were obtained before and after the training period. Three different tests were carried out for sEMG evaluations: Test A tried to elicit a voluntary maximal isometric contraction (VMIC); Test B used NMES to elicit a muscular contraction; and Test C used 1 ms repetitive electrical impulses to elicit a series of M-waves. Muscle strength was evaluated from the root mean square (RMS; Tests A and B) and peak to peak (PP; Test C) values obtained from the sEMG data. Measures related to amplitude (RMSa; RMSm; RMSa; PP) were normalised with their pre-training values for every muscle prior to statistical analysis, leading to values as a proportion of the initial value. The evaluation of muscle fatigue was performed using a Fourier test analysis of the frequency range, obtaining the median frequency (MF) for all tests. Muscle thickness (MT) was measured by ultrasound of left and right sides of the rectus abdominis. Data were analysed using non parametric test of Wilcoxon (Test A RMSa; Test A RMSm; Test B RMSa; Test B RMSm; Test C PP; MT; P<0.05) and t-test (Test A MF; Test B MF; Test C MF; P<0.05). Statistical differences were observed between baseline and trained horses. Muscle force increase following NMES training in Test B (RMSa 2.50±0.69; RMSm 2.59±0.76) and Test C (PP 5.20±1.76). Fatigue of the rectus abdominis decreased in Test A (168.33±55.19 vs 232.63±44.15 Hz) and Test C MF (187.93±20.76 vs 236.98±52.39 Hz), but not in Test B (363.98±45.48 vs 327.95±50.84 Hz). The difference in muscle thickness between the two groups was not significant (10.96±0.64; 11.78±0.79 mm). The results suggest that NMES training could be used as an effective method to increase muscle force and fatigue resistance of the rectus abdominis muscle in the horse.

Periodical sparse low-rank matrix estimation algorithm for fault detection of rolling bearings

Early bearing fault detection is crucial to avoid catastrophic accidents. However, the repetitive defect impulses indicating bearing fault are buried in heavy background noise. In the paper, a novel periodical sparse low-rank (PSLR) matrix estimation algorithm is proposed for extracting repetitive transients from noisy signal. Concretely, periodical group sparsity and low-lank property of fault transients in time–frequency domain are first revealed, and then an optimization problem is proposed for simultaneously promoting these two properties. Meanwhile, to further highlight the sparsity of fault features, the non-convex penalty functions are incorporated into the optimization problem. Then, for solving the proposed optimization problem, an iterative algorithm is derived based on alternating direction method of multipliers (ADMM) and majorization–minimization (MM), in which the traditional soft-thresholding operation is replaced by the proposed Gini-guided fault information thresholding (FIT) scheme to enhance fault transient extraction. Finally, simulated and real signals confirm the performance of proposed PSLR in extracting defect impulses from noisy vibration signal.

Electrical treeing in silicone gel under repetitive voltage impulses

Silicone gel is widely used to encapsulate power electronic modules. The weakness of the electrical insulation is surface discharges initiated at the gel-substrate-electrode triple junction and the subsequent formation of cavities; so called electrical trees. The propagation characteristics of cavities under high frequency and fast rise time voltages, which are typical waveforms formed in power electronic modules, have not been fully understood. Thus, in this research, the influence of frequency, rise time, and polarity on electrical treeing under repetitive voltage impulses are investigated. The results show that discharge tracks under positive impulses have filamentary shapes, and discharge tracks under negative impulses have spindle-shapes. The cavity length increases with the frequency and is especially long when the rise time is short under high frequency. This indicates the importance of understanding the cavity propagation characteristics under actual voltage waveforms in power electronics modules.

Identification of Bearing Faults using Wavelet Transform

This research is concerned with description of a scheme for bearing’s localized defect detection based on wavelet packet transform (WPT). WPT provides a high resolution time-frequency distribution from which periodic structural ringing due to repetitive force impulses, generated upon the passing of each rolling element on the defect, are detected. The objective of this work is to emphasis on the outer race defect, inner race defect and ball defect. In modern industrial scenario, there is increasing demand for automatic condition monitoring that reduce the gap between digital model and actual product. With reliable condition monitoring, faults such as machine element failures could be identified in their early-stages and further damage to the system could be prevented. Successful monitoring is a complex and application-specific problem, but a generic tool would be useful in preliminary analysis of new signals and in verification of known theories.

Weak Crack Detection for Gearbox Using Sparse Denoising and Decomposition Method

The localized faults of rotating machinery can be diagnosed by the extraction of the periodic transient impulses (PTIs); however, the PTI is generally weak and may get submerged in strong noise. To address this issue, a novel approach based on nonconvex sparse regularization denoising and adaptive sparse decomposition is proposed. The main work can be divided into two areas: 1) raw signal denoising and 2) repetitive impulses isolation. Specifically, for the raw signal denoising, the augmented Huber function is proposed as penalty function, and the convexity of the objective cost function (OCF) can be maintained; meanwhile, the solution of the proposed OCF can be solved using forward–backward splitting algorithm. For the repetitive impulses isolation, due to the commonly used resonance-based signal sparse decomposition (RSSD) method lacks adaptability and flexibility in practical application, the genetic algorithm (GA) is introduced to optimize the decomposition parameters of the RSSD that are selected adaptively in the desirable range according to the denoised signal in terms of the global optimization characteristic of GA. As an example, a pinion gear with weak root-crack failure is investigated based on the proposed approach. Compared to some state-of-the-art methods such as L1-norm fused lasso optimization (LFLO) and maximum correlated kurtosis deconvolution method, the results demonstrate that the proposed approach can effectively extract the weak fault frequency and its harmonics, and the shortcoming of the systematic underestimation of LFLO method has also been greatly improved.

SU71 - BDNF GENE AND OBSESSIVE COMPULSIVE DISORDER – DO SYMPTOM DIMENSIONS HAVE SPECIFIC GENETIC CORRELATES?

Background Obsessive Compulsive Disorder (OCD) is a common neuropsychiatric disorder with a worldwide 12-month prevalence ranging from 1% to 2% across studies (Weissman et al., 1995; Ruscio et al., 2010). Characterized by repetitive intrusive thoughts, images and impulses (obsessions) and repetitive behaviors or mental acts (compulsions), the clinical phenotype of OCD appears to be complex. Genetic etiology of Obsessive Compulsive Disorder (OCD) has been investigated extensively, with mixed results across candidate gene studies. In this study, we perform a case control analysis of association of a single nucleotide polymorphism rs6265(Val66Met) in Brain Derived Neurotrophic Factor gene, that has been previously implicated in a variety of psychiatric syndromes, and examine its association with symptom dimensions of OCD. Methods Patients (n=377) diagnosed with OCD by DSM IV – TR were recruited from a specialty OCD clinic at the NIMHANS, Bengaluru, INDIA. Patients were assessed on socio-demographic data, detailed history of illness, presence of common co-morbid disorders, family history of OCD and major psychiatric disorders, and treatment details. Patients were additionally assessed on Yale Brown Obsessive Compulsive Scale (YBOCS), Mini International Neuropsychiatric Interview plus (MINI plus) Clinical Global Impression scale (CGI). Control population (n=449) consisted of healthy individuals also of south Indian origin. MINI plus was administered to screen and rule out psychiatric diagnosis in this group. DNA samples were genotyped for polymorphism rs6265 (Val66Met) using Quantitative Real time PCR. Results Factor analysis yielded five factor solutions as the best fit for the data explaining 65% of the variance as follows - Factor-1 with robust loading on hoarding obsession (0.891) & collecting compulsions (0.913). Factor-2 with Obsessions of need for symmetry (0.821) & ordering compulsions (0.864). Factor-3 with Pathological doubts obsessions (0.824) & checking compulsions (0.819). Factor-4 with Sexual (0.680), religious (0.655) &aggressive obsessions (0.554), mental rituals compulsions (0.630) and Factor-5 with Contamination obsessions (0.919) & washing compulsions (0.893). The allele ‘A’ frequency was found to be significantly higher in the controls, as compared to cases suggesting a protective effect. Among the clinical variables tested in univariate analysis by genotype, factor-5 (contamination obsessions with washing compulsions) and factor 1 (hoarding), dimension scores was significantly lower in ‘A’ carriers in subjects with OCD compared to homozygous G carriers (GG). Discussion Our findings suggest a protective effect of the rarer Met allele in OCD. The lower factor-5 scores in Met carrier patients and its confirmation after controlling for the confounders further suggests a probable specific protective effect in contamination-washing dimension of OCD. This dimension, which is one of the most commonly observed clinical phenotype, may represent a more homogenous group, with specific biological underpinnings. Functional imaging studies show that the same sub-group of OCD patients demonstrate specific regional activation in ventromedial prefrontal regions in symptom provocation paradigm (Mataix-Cols et al., 2004). The Met allele of rs6265 has also been associated with volumetric reduction in the same region (Yu et al., 2009). These findings suggest a need for future imaging and genetic studies in specific symptom dimensions of OCD.

Obsessive-Compulsive Disorder.

This article reviews current knowledge regarding diagnosis, pathophysiology, and treatment trends in obsessive-compulsive disorder (OCD), a severe, underrecognized, and chronic condition frequently encountered in neurologic practice. With a lifetime prevalence estimated at 2.5%, OCD is a common condition that can also present comorbidly with neurologic disease. The core symptoms of OCD are obsessions and compulsions. Obsessions are intrusive repetitive thoughts, urges, images, or impulses that trigger anxiety and that the individual is not able to suppress. Compulsions are repetitive behaviors or mental acts occurring in response to an obsession with the intention of reducing the distress caused by obsessions. Neuroimaging, neuropsychological, and pharmacologic studies suggest that the expression of OCD symptoms is associated with dysfunction in a cortico-striato-thalamo-cortical circuit. Evidence-based treatments for OCD comprise pharmacotherapy and cognitive-behavioral therapy. Selective serotonin reuptake inhibitors (SSRIs) are the first-line drugs recommended for OCD, but significant differences exist in their use for OCD compared to their use for other mood and anxiety conditions, including the need for higher dosage, longer trials necessitated by a longer lag for therapeutic response, and typically lower response rates. Cognitive-behavioral therapy, based on the principles of exposure and response prevention, shows results superior to pharmacologic treatments with lower relapse rates on long-term follow-up and thus should be considered in the treatment plan of every patient with OCD. OCD and obsessive-compulsive symptoms are frequently encountered in the neurologic clinic setting and require a high index of suspicion to effectively screen for them and an illness-specific therapeutic approach.

Multiband Envelope Spectra Extraction for Fault Diagnosis of Rolling Element Bearings.

Bearing fault features are presented as repetitive transient impulses in vibration signals. Narrowband demodulation methods have been widely used to extract the repetitive transients in bearing fault diagnosis, for which the key factor is to accurately locate the optimal band. A multitude of criteria have been constructed to determine the optimal frequency band for demodulation. However, these criteria can only describe the strength of transient impulses, and cannot differentiate fault-related impulses and interference impulses that are cyclically generated in the signals. Additionally, these criteria are easily affected by the independent transitions and background noise in industrial locales. Therefore, the large values of the criteria may not appear in the optimal frequency band. To overcome these problems, a new method, referred to as multiband envelope spectra extraction (MESE), is proposed in this paper, which can extract all repetitive transient features in the signals. The novelty of MESE lies in the following aspects: (1) it can fuse envelope spectra at multiple narrow bands. The potential bands are selected based on Jarque-Bera statistics of narrowband envelope spectra; and (2) fast independent component analysis (fastICA) is introduced to extract the independent source spectra, which are fault- or interference-related. The multi-band strategy will preserve all impulse features and make the method more robust. Meanwhile, as a blind source separation technique, the fastICA can suppress some in-band noise and make the diagnosis more accurate. Several simulated and experimental signals are used to validate the efficiency of the proposed method. The results show that MESE is effective for enhanced fault diagnosis of rolling element bearings. Bearing faults can be detected even in a harsh environment.

A New Family of Model-Based Impulsive Wavelets and Their Sparse Representation for Rolling Bearing Fault Diagnosis

The localized faults of rolling bearings can be diagnosed by the extraction of the impulsive feature. However, the approximately periodic impulses may be submerged in strong interferences generated by other components and the background noise. To address this issue, this paper explores a new impulsive feature extraction method based on the sparse representation. According to the vibration model of an impulse generated by the bearing fault, a novel impulsive wavelet is constructed, which satisfies the admissibility condition. As a result, this family of model-based impulsive wavelets can form a Parseval frame. With the model-based impulsive wavelet basis and Fourier basis, a convex optimization problem is formulated to extract the repetitive impulses. Based on the splitting idea, an iterative thresholding shrinkage algorithm is proposed to solve this problem, and it has a fast convergence rate. Via the simulated signal and real vibration signals with bearing fault information, the performance of the proposed approach for repetitive impulsive feature extraction is validated and compared with the noted spectral kurtosis method, the optimized spectral kurtosis method based on simulated annealing, and the resonance-based signal decomposition method. The results demonstrate its advantage and superiority in weak repetitive transient feature extraction.

The effect of inverter characteristics on partial discharge and life behavior of wire insulation

It has been demonstrated already that adjustable speed drives, and, in general, power electronics waveforms, can harm insulation systems of rotating machines, causing significant reduction of reliability and life. Indeed, the type of supply voltage made by repetitive impulses alters significantly the electric field distribution in the turn-to-turn and phase-to-ground insulation, and also the machine terminal peak voltage can be increased significantly, with respect to the expected value at the inverter output [1]–[11].

Effect of common antivertiginous agents on the high velocity vestibulo-ocular reflex

ObjectiveIt has long been suggested that antivertiginous medications exert their symptomatic effect through inhibition of the vestibulo-ocular reflex (VOR). We tested this hypothesis by directly measuring the VOR after administration of three agents from different substance classes: an antihistamine, a benzodiazepine and a calcium channel antagonist. MethodsThe gain and the variability of the high velocity VOR was assessed using video head impulses (vHIT) under the following conditions: baseline, after dimenhydrinate, after diazepam and after cinnarizine. ResultsWe found that all three medications did not change any VOR gain or variability parameter: At 60ms, the gain was 0.95 at baseline, 0.99 under dimenhydrinate, 0.99 under diazepam and 0.96 under cinnarizine. The gain variability across repetitive head impulses remained also uninfluenced. ConclusionsThe human high frequency VOR remains robust to pharmacological perturbations at common clinical doses and the assumption that symptomatic vertigo relief is achieved merely through impairment of the VOR requires re-examination. SignificanceAlternative mechanisms of pharmacological action might be operant, such as the modulation of vestibulo-cortical pathways, a differential effect on the low frequency VOR and an altered sensitivity to drugs in acute unilateral vestibulopathy.

Spectral negentropy based sidebands and demodulation analysis for planet bearing fault diagnosis

Planet bearing vibration signals are highly complex due to intricate kinematics (involving both revolution and spinning) and strong multiple modulations (including not only the fault induced amplitude modulation and frequency modulation, but also additional amplitude modulations due to load zone passing, time-varying vibration transfer path, and time-varying angle between the gear pair mesh lines of action and fault impact force vector), leading to difficulty in fault feature extraction. Rolling element bearing fault diagnosis essentially relies on detection of fault induced repetitive impulses carried by resonance vibration, but they are usually contaminated by noise and therefor are hard to be detected. This further adds complexity to planet bearing diagnostics. Spectral negentropy is able to reveal the frequency distribution of repetitive transients, thus providing an approach to identify the optimal frequency band of a filter for separating repetitive impulses. In this paper, we find the informative frequency band (including the center frequency and bandwidth) of bearing fault induced repetitive impulses using the spectral negentropy based infogram. In Fourier spectrum, we identify planet bearing faults according to sideband characteristics around the center frequency. For demodulation analysis, we filter out the sensitive component based on the informative frequency band revealed by the infogram. In amplitude demodulated spectrum (squared envelope spectrum) of the sensitive component, we diagnose planet bearing faults by matching the present peaks with the theoretical fault characteristic frequencies. We further decompose the sensitive component into mono-component intrinsic mode functions (IMFs) to estimate their instantaneous frequencies, and select a sensitive IMF with an instantaneous frequency fluctuating around the center frequency for frequency demodulation analysis. In the frequency demodulated spectrum (Fourier spectrum of instantaneous frequency) of selected IMF, we discern planet bearing fault reasons according to the present peaks. The proposed spectral negentropy infogram based spectrum and demodulation analysis method is illustrated via a numerical simulated signal analysis. Considering the unique load bearing feature of planet bearings, experimental validations under both no-load and loading conditions are done to verify the derived fault symptoms and the proposed method. The localized faults on outer race, rolling element and inner race are successfully diagnosed.

Human neurophysiology to diagnose, prevent and treat pain

Neurophysiological techniques are useful to diagnose, predict and treat chronic pain, in particular of neuropathic origin. In this talk we shall succinctly review these different approaches, as well as the possibility they open to «back translation» of results gathered in humans to animal models. Definite diagnosis of neuropathic pain (NP) needs confirmation of a lesion or a disease of somatosensory systems, and neurophysiology is crucial to obtain such information. Lesions responsible for NP relate essentially to small fibres A-delta and C in the periphery, and the spino-thalamo-cortical system of pain/temperature at central levels. Among the methods to record responses in pain/temperature pathways, evoked potentials to laser (LEPs) or contact thermodes (CHEPs) are simple and reliable. Their diagnostic efficacy increases when they are coupled to vegetative reactions (SSR), psychophysical measures (reaction times, quantitative sensory tests), and concomitant exploration of non-nociceptive systems (large fibres, dorsal columns). The diagnostic workout should ideally respond to: (a) is there objective somatosensory impairment in painful regions? (b) which is the respective alteration of different sub-systems? (c) are the results concordant with imaging? and (d) how do results aid to patient management? Identification/prevention of neural lesions during surgery can minimise the development of postoperative pain. Joint analysis of nociceptive cortical responses and MRI allowed to classify ∼90% of patients with central pain post-thalamic stroke, and time-frequency LEP analysis may distinguish patients with and without pain following brainstem lesions. All these elements suggest that early neurophysiological studies of patients at risk may help to detect those with highest probability of developing NP, and hence open the way to anticipatory therapy. Recording of sympathetic skin or pupillary responses allows assessing the vegetative arousal provoked by a painful stimulus, indicating that the stimulus was actually perceived. Association and dissociation between vegetative and cortical responses are especially useful in the objective evaluation of allodynic phenomena and clinically dubious perception anomalies. Current trends aim at developing neurophysiological indexes of nociception in non-communicating patients (coma, general anaesthesia). Non-invasive cortical stimulation using magnetic repetitive impulses (rTMS) or scalp-applied currents (tDCS, tACS) are used not only to predict efficacy of subsequent neurosurgical stimulation, but also, increasingly, as stand-alone methods of pain relief. Recent developments allow certain of these techniques to be applied by the patients themselves at home, via internet-based interfacing and control from the hospital.