Non-stationary Speed Research Articles

Enhanced algorithm for predictive maintenance to detect turbocharger overspeed in diesel engine rail vehicles

The reliability and safety of locomotives is crucial for efficient train operation. Repeated turbocharger failures in Israel Railways locomotive fleet have raised serious safety concerns. An investigation into the failures revealed that the uncontrolled acceleration and overspeed transients of the turbocharger shaft occurred before the failure. Early detection of potential turbocharger failures by predicting overspeed conditions is critical to the safety and reliability of locomotives. In this study, an enhanced novel algorithm for estimating the Instantaneous Angular Speed (IAS) of the turbocharger and diesel engines is presented to overcome the challenges of transient operating conditions of diesel engines. Using adaptive dephasing, the algorithm effectively isolates critical asynchronous vibration components that are crucial for the early detection of turbocharger failures. This algorithm is suitable for non-stationary speeds and is applicable to any range of rotational speed and rate of change. The algorithm requires the input of the basic parameters of the system, while all other parameters that control the process are determined automatically. The algorithm was developed specifically for the special operating conditions of diesel engines and improves predictive maintenance and operational reliability. The method is robust as it correlates between several characteristic frequencies of the rotating parts of the system. The algorithm was verified and validated with simulated and experimental data.

Matching and reassignment based time-frequency enhancement for rotating machinery fault diagnosis under nonstationary speed operations

Time-frequency (TF) analysis (TFA) to nonstationary signals can reveal the nonlinearly changing instantaneous frequencies (IFs) of signals; it is, therefore, widely used for rotating machinery fault diagnosis under time-varying speed conditions. However, the traditional TFA methods may only reveal the outline of IFs due to the extra TF diffusion caused by limited TF resolution, hindering the fault diagnosis of rotating machinery. To enhance the readability of the time-frequency representation (TFR) of the signals with IF trajectories linearly time-varying, linear chirplet transform has been proved effective. To effectively tackle the signal with nonlinearly changing IFs, a string of chirp-rates is preferred, where the final TFR is obtained by superposition of each corresponding sub-TFR at each TF point. However, the extra cross-term interferences resulted from a string of chirp-rates cannot be neglected on TFRs. Aiming at alleviating the cross-terms from the TFR, matching and reassignment based TF enhancement strategy is proposed, where only the appropriate chirp-rate and its corresponding TFR slice is retained at each time instant. The appropriate chirp-rate is adaptively selected by the index—spectral kurtosis. To further increase the readability of the resulting TFR, a reassignment technique synchrosqueezing transform is integrated with the proposed matching strategy. By iteratively employing reassignments, TFR with the enhanced energy and sharp IF ridges can be generated. The effectiveness of the proposed method is validated by both simulated and experimental analyses. It is shown that the proposed method is effective in processing time-varying signals and can provide more accurate IF estimation, which paves the way for rotating machinery fault diagnosis under nonstationary speed conditions.

A Health Data Map-Based Ensemble of Deep Domain Adaptation Under Inhomogeneous Operating Conditions for Fault Diagnosis of a Planetary Gearbox

A deep learning model trained under a specific operating condition of the gearbox often experiences an overfitting problem, which makes it impossible to diagnose faults under different operating conditions. To solve this problem, this paper proposes an ensemble of deep domain adaptation approaches with a health data map. As a fundamental approach to alleviate the domain shift problem due to inhomogeneous operating conditions, the vibration signal is transformed into an image-like simplified health data map that visualizes a tooth-wise fault of the gearbox. The simplified health data map enables the use of a conventional convolutional neural network (CNN) model. To solve the remaining domain shift problem even with the simplified health data map, this study employs a maximum classifier discrepancy (MCD), which is a typical domain adaptation method. To further enhance its performance, a discrepancy-scale factor-based MCD and its ensemble approach are proposed. The proposed method is demonstrated with a 2 kW planetary gearbox testbed operated under stationary and non-stationary speed conditions. The results present that the proposed method outperforms conventional CNN and MCD even under the inhomogeneous operating condition of the gearbox.

Interturn short‐circuit fault diagnosis in PMSM with partitioned stator windings

A new signal-based strategy for stator winding interturn short-circuit fault detection and isolation in permanent magnet synchronous machines (PMSM) is proposed. The strategy is based on an analysis of the midpoint voltage of the phase windings. A PMSM model with partitioned stator windings including an interturn short-circuit fault is presented. Unlike the motor current signature analysis methods, the midpoint voltage measurement allows detecting an incipient stator fault even under external disturbances without false alarms. For PMSM generators used in variable-speed wind turbines, the random changes of wind produce fluctuations in the rotor speed. In this situation, conventional fault detection methods used for the steady-state condition may produce errors in the fault diagnosis. However, the proposed strategy is capable of rejecting the effects of the non-stationary speed conditions by resampling the voltage signals at fixed increments of the rotor angular position. Several experimental results in time and frequency-domain are presented for different speed conditions using a PMSM with modified stator winding.

GA-Adaptive Template Matching for Offline Shape Motion Tracking Based on Edge Detection: IAS Estimation from the SURVISHNO 2019 Challenge Video for Machine Diagnostics Purposes

The estimation of the Instantaneous Angular Speed (IAS) has in recent years attracted a growing interest in the diagnostics of rotating machines. Measurement of the IAS can be used as a source of information of the machine condition per se, or for performing angular resampling through Computed Order Tracking, a practice which is essential to highlight the machine spectral signature in case of non-stationary operational conditions. In these regards, the SURVISHNO 2019 international conference held at INSA Lyon on 8–10 July 2019 proposed a challenge about the estimation of the instantaneous non-stationary speed of a fan from a video taken by a smartphone, a pocket, low-cost device which can nowadays be found in everyone’s pocket. This work originated by the author to produce an offline motion-tracking of the fan (actually, of the head of its locking-screw) and obtaining then a reliable estimate of the IAS. The here proposed algorithm is an update of the established Template Matching (TM) technique (i.e., in the Signal Processing community, a two-dimensional matched filter), which is here integrated into a Genetic Algorithm (GA) search. Using a template reconstructed from a simplified parametric mathematical model of the features of interest (i.e., the known geometry of the edges of the screw head), the GA can be used to adapt the template to match the search image, leading to a hybridization of template-based and feature-based approaches which allows to overcome the well-known issues of the traditional TM related to scaling and rotations of the search image with respect to the template. Furthermore, it is able to resolve the position of the center of the screw head at a resolution that goes beyond the limit of the pixel grid. By repeating the analysis frame after frame and focusing on the angular position of the screw head over time, the proposed algorithm can be used as an effective offline video-tachometer able to estimate the IAS from the video, avoiding the need for expensive high-resolution encoders or tachometers.

Time-Frequency demodulation analysis via Vold-Kalman filter for wind turbine planetary gearbox fault diagnosis under nonstationary speeds

Abstract Wind turbine planetary gearbox fault diagnosis under nonstationary speeds is a challenging topic, because of the high complexity and strong time variability of vibration signals. In order to resolve time-varying gear fault features, a quality time-frequency analysis method is in demand. Time-frequency representations based on Hilbert transform and analytic signal approach have fine time-frequency resolution, and are free from both outer (cross-term) and inner (auto-term) interferences, thus providing an effective approach to nonstationary signal analysis. However, they rely on accurate instantaneous frequency estimation, and thereby are subject to mono-component constraint. To address this issue, Vold-Kalman filter is exploited to construct time-frequency representation, by virtue of its capability to decompose the multi-component vibration signal of rotating machinery into constituent mono-component harmonic waves. Even so, intricate time-varying sidebands inherent with raw planetary gearbox vibration signals in joint time-frequency domain are still a hurdle, because they do not link to gear fault frequency directly. To solve this problem, the proposed time-frequency analysis method is further extended to generate time-varying amplitude and frequency demodulated spectra, inspired by the fact that gear fault frequency is manifested straight by the amplitude and frequency modulating frequencies. The proposed method is illustrated by numerical simulation, and is further validated using lab experimental signals of a wind turbine planetary gearbox. Both the localized and distributed faults on gears are successfully diagnosed under nonstationary speeds.

Vibration-based angular speed estimation for multi-stage wind turbine gearboxes

Most processing tools based on frequency analysis of vibration signals are only applicable for stationary speed regimes. Speed variation causes the spectral content to smear, which encumbers most conventional fault detection techniques. To solve the problem of non-stationary speed conditions, the instantaneous angular speed (IAS) is estimated. Wind turbine gearboxes however are typically multi-stage gearboxes, consisting of multiple shafts, rotating at different speeds. Fitting a sensor (e.g. a tachometer) to every single stage is not always feasible. As such there is a need to estimate the IAS of every single shaft based on the vibration signals measured by the accelerometers. This paper investigates the performance of the multi-order probabilistic approach for IAS estimation on experimental case studies of wind turbines. This method takes into account the meshing orders of the gears present in the system and has the advantage that a priori it is not necessary to associate harmonics with a certain periodic mechanical event, which increases the robustness of the method. It is found that the MOPA has the potential to easily outperform standard band-pass filtering techniques for speed estimation. More knowledge of the gearbox kinematics is beneficial for the MOPA performance, but even with very little knowledge about the meshing orders, the MOPA still performs sufficiently well to compete with the standard speed estimation techniques. This observation is proven on two different data sets, both originating from vibration measurements on the gearbox housing of a wind turbine.

Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation

Signals captured in rotating machines to obtain the status of their components can be considered as a source of massive information. In current methods based on artificial intelligence to fault severity assessment, features are first generated by advanced signal processing techniques. Then feature selection takes place, often requiring human expertise. This approach, besides time-consuming, is highly dependent on the machinery configuration as in general the results obtained for a mechanical system cannot be reused by other systems. Moreover, the information about time events is often lost along the process, preventing the discovery of faulty state patterns in machines operating under time-varying conditions. In this paper a novel method for automatic feature extraction and estimation of fault severity is proposed to overcome the drawbacks of classical techniques. The proposed method employs a Deep Convolutional Neural Network pre-trained by a Stacked Convolutional Autoencoder. The robustness and accuracy of this new method are validated using a dataset with different severity conditions on failure mode in a helical gearbox, working in both constant and variable speed of operation. The results show that the proposed unsupervised feature extraction method is effective for the estimation of fault severity in helical gearbox, and it has a consistently better performance in comparison with other reported feature extraction methods.

Semi Empirical Model in the Problem of Non-Stationary Burning of Propellant

Рассматривается задача о выборе зависимости температуры на поверхности твердого топлива от уровня давления в камере сгорания. Постановка задачи основана на применении модели Зельдовича - Новожилова с привлечением экспериментальных результатов о нестационарной скорости горения твердого топлива. Задача выбора ставится как задача условной оптимизации, в которой целевая функция определяется разностью скоростей горения топлива, определяемых экспериментально и численно решением уравнения теплопроводности. Решение задачи оптимизации выполняется методами Ньютона, деформируемого многогранника и генетического алгоритма. Корректность принятой постановки задачи показана решением тестовой задачи. Приводится сравнительный анализ эффективности применения различных методов оптимизации для определения коэффициентов, входящих в математическую модель нестационарной скорости горения твердого топлива.

Eccentricity fault diagnosis in a permanent magnet synchronous motor under nonstationary speed conditions

Eccentricity fault diagnosis in a permanent magnet synchronous motor under nonstationary speed conditions

Introducing angularly periodic disturbances in dynamic models of rotating systems under non-stationary conditions

A mechanical defect in a rotating system can be interpreted as the presence of a periodic angular perturbation. Understanding the interactions between the defect and its consequences on measurable quantities representative of dynamic behavior (acceleration, speed, etc.) necessarily involves the implementation of numerical models. The aim of this paper is to present a model suitable for introducing this kind of disturbance in the case of systems operating under non-stationary speed conditions. This model corresponds to a numerical extension of approaches increasingly used to analyze experimental signals.The resulting model can be used to investigate of the influence of disturbance parameters on the dynamic responses of the rotating system. It also allows comparisons of new assumptions on the origin of this perturbation. This new approach is illustrated by using the general framework of bearing faults.

A New Approach to Fault Diagnostics for Permanent Magnet Synchronous Machines Using Electromagnetic Signature Analysis

This paper proposes a novel approach to health monitoring and multifault detection in permanent magnet synchronous machines using direct flux measurement with search coils. Unlike other spectrum-based fault detection schemes, only the fundamental frequency component of the measured voltage is utilized for fault detection. Therefore, the performance of the proposed scheme is not affected by nonstationary speed or harmonics introduced by the power supply. In addition, location of interturn short circuits and direction of static eccentricity can be detected, which have never been done by any other scheme. In spite of the invasive nature of the technique, it is very suitable for mission-critical applications and emerging applications such as off-shore wind turbines, hybrid vehicle technology, and military applications, where early detection of faults is of paramount importance. 2-D simulations using finite element analysis have been presented to validate the proposed method under different operating conditions. Experimental introduction of stator interturn short circuit, demagnetization, and static eccentricity has been discussed, and the proposed scheme is experimentally implemented to examine its effectiveness.

Diagnosis of Interturn Faults in PMSMs Operating Under Nonstationary Conditions by Applying Order Tracking Filtering

Interturn faults in permanent magnet synchronous machines may have very harmful effects if not early identified. This study deals with the detection of such faults when the machine operates under varying speed conditions. The performance of two methods is analyzed and compared, i.e., the analysis of the third harmonic of the stator currents and the first one of the zero-sequence voltage components. The Vold-Kalman filtering order tracking algorithm is introduced and applied to track the harmonics of interest when the machine operates under a wide speed range and different load levels. This study also presents two reliable fault indicators especially focused to detect stator winding interturn faults under nonstationary speed conditions. Experimental results endorse the methodology proposed, showing its potential to carry out a reliable fault diagnosis scheme.

Demagnetization diagnosis in permanent magnet synchronous motors under non-stationary speed conditions

Permanent magnet synchronous motors (PMSMs) are applied in high performance positioning and variable speed applications because of their enhanced features with respect to other AC motor types. Fault detection and diagnosis of electrical motors for critical applications is an active field of research. However, much research remains to be done in the field of PMSM demagnetization faults, especially when running under non-stationary conditions. This paper presents a time–frequency method specifically focused to detect and diagnose demagnetization faults in PMSMs running under non-stationary speed conditions, based on the Hilbert Huang transform. The effectiveness of the proposed method is proven by means of experimental results.