Non-stationary Operating Conditions Research Articles

Fault diagnosis of bearings in multiple working conditions based on adaptive time-varying parameters short-time Fourier synchronous squeeze transform

Rolling bearings are commonly used components in rotating machinery and play a vital role. When the bearing fails, if it cannot be found and repaired in time, it will cause great economic losses. Time-frequency analysis has been widely used for bearing fault signals under non-stationary operating conditions, but the existing methods have problems such as poor adaptability under multiple operating conditions. At the same time, the low time-frequency resolution and poor energy aggregation also affect the fault feature extraction effect. Aiming at these problems, this paper proposes a bearing fault detection method, which combines empirical mode decomposition and adaptive time-varying parameter short-time Fourier synchronous squeezing transform (AFSST), it solves the problem of adapting to signals under multiple operating conditions; A weighted least squares estimation time-varying parameter algorithm is proposed, which improves the calculation speed by 29% under the premise of ensuring the calculation accuracy; A time-varying index of energy effective compression ratio is proposed to accurately measure the time-varying energy aggregation of time-frequency analysis methods. Using short-time Fourier transform, continuous wavelet transform, wavelet synchrosqueezed transform, and AFSST to analyze the simulated FM signal, the results show that the AFSST transform has better time-frequency resolution and higher energy-efficient compression rate globally. Through the verification of the fault experimental data of rolling bearings, the diagnosis method proposed in this paper can accurately extract the bearing fault characteristics, has a good diagnosis ability in the multi-working operating environment, and has strong robustness and anti-noise interference.

Traversal index enhanced-gram (TIEgram): A novel optimal demodulation frequency band selection method for rolling bearing fault diagnosis under non-stationary operating conditions

It is very important to select the optimal demodulation frequency band (ODFB) of rolling bearing vibration signals for the most valuable fault information extraction and diagnosis. Fast kurtogram (FK) is an effective and most commonly used ODFB selection approach for bearing fault diagnosis, which generally is founded on the filter bank structure and short-time Fourier transform. Though the FK method can effectively detect the shock characteristics of frequency band signals, other useful characteristics related with failure of vibration signal will be ignored. In this paper, a novel ODFB selection method called traversal index enhanced-gram (TIEgram) is proposed for rolling bearing vibration signals. In the proposed TIEgram method, first of all, the traversal segmentation model is utilized to transfer the original signal into frequency domain for enhancing overall segmentation performance of traditional binary trees and 1/3 binary trees structure segmentation models. Then, a new weighted fusion indicator based on the kurtosis, correlation coefficient and spectral negative entropy is designed to select ODFB from the segmented results of traversal segmentation model, which can effectively solve the problem that different vibration signal characteristics cannot be fully detected by a single indicator. After that, an enhanced adaptive multi-scale weighted morphological filtering-based envelope spectrum is employed to demodulate the obtained frequency band for a much more accurate diagnosis effect of rolling bearing. Finally, the simulated and measured signals of rolling bearing under stationary and non-stationary operating conditions are respectively used to verify the feasibility and effectiveness of the proposed method with comparison of the existing FK, Autogram and infogram methods. The comparison analysis results show that TIEgram method can accurately identify the most useful fault information and shows better performance than existing methods.

Gearbox Multiple Faults Diagnosis under Stationary and Non-Stationary Operating Conditions Using Convolutional Neural Networks

Gearbox Multiple Faults Diagnosis under Stationary and Non-Stationary Operating Conditions Using Convolutional Neural Networks

Long-term reproducibility of emission characteristics of high-current diamond-graphite sources of electrons with field emission under non-stationary vacuum operating conditions

The results of a study of the long-term reproducibility of the emission characteristics of electron sources with field emission density of up to 1000 A/cm2 of based on composite nanocarbon films are presented. It was found that high-current field emission is accompanied by the sputtering of residual gas ions from atoms of the cathode material and its sedimentation on the anode. The results can be used to predict the durability of high-current diamond-graphite cathodes with field emission when operating in unsteady temperature-vacuum conditions. Keywords: diamond-graphite nanocomposite, high-current field emission, reproducibility of field emission parameters, service life of cathode with field emission.

Fast Simulation of Analog Circuit Blocks Under Nonstationary Operating Conditions

This article proposes a black-box behavioral modeling framework for analog circuit blocks (CBs) operating under small-signal conditions around nonstationary operating points. Such variations may be induced either by changes in the loading conditions or by event-driven updates of the operating point for system performance optimization, e.g., to reduce power consumption. An extension of existing data-driven parameterized reduced-order modeling techniques is proposed, which considers the time-varying bias components of the port signals as nonstationary parameters. These components are extracted at runtime by a low-pass filter and used to instantaneously update the matrices of the reduced-order state-space model realized as a SPICE netlist. Our main result is a formal proof of quadratic stability of such linear parameter varying (LPV) models, enabled by imposing a specific model structure and representing the transfer function in a basis of positive functions whose elements constitute a partition of unity. The proposed quadratic stability conditions are easily enforced through a finite set of small-size linear matrix inequalities (LMIs), used as constraints during model construction. Numerical results on various CBs, including voltage regulators, confirm that our approach not only ensures the model stability but also provides speedup in runtime up to two orders of magnitude with respect to full transistor-level circuits.

A A Pilot Review of State-of-the-Art Deep Learning Applications to Nuclear Engineering & Technology

Given the advent of big data (BD), the focus of this “pilot” review of the most recent literature is upon deep learning (DL) implementations/solutions to important, as well as inherently complex, nuclear engineering and technology (NET) problems. The potential advancement from implementing large/real-scale DL-based algorithms is critically detailed, as well as highlighted, by inspecting the relevant body of latest research which appears in two major – overlapping, yet also complementary to each other in terms of scope and audience - nuclear energy (NE)-dedicated scientific research means of publication, namely Elsevier’s “Annals of Nuclear Energy” and “Progress in Nuclear Energy” journals. It is demonstrated that, given their (I) accuracy, robustness-to-noise and resilience to incomplete/erroneous information, as well as (II) competence under non-stationary and transient nuclear reactor (NR) operating conditions, deep and convolutional neural networks (DNNs and CNNs, respectively), as well as their variants, are becoming increasingly important, in their own right, for the swift and reliable solution of NET problems. It will be of great interest for – while also advantageous to - the NE, BD, and computational intelligence (CI)/DL communities, to join forces, follow up, and contribute to future research in – as well as application to - this particularly interesting and fruitful collaboration between disciplines.

An adaptive order-band energy ratio method for the fault diagnosis of planetary gearboxes

Condition monitoring of planetary gearboxes in time-varying speed service is an important and actual engineering requirement, however, also an intractable task. The traditional sideband energy ratio (SER) method limits its application to complex operational conditions due to the frequency spectrum based and manual bandwidth selection. To this end, this paper proposes an adaptive order-band energy ratio (AOER) method, an enhanced version of the SER, to quantitatively and intelligently diagnose gear faults under different operational conditions. First, a signal model based on angular increment and order components is proposed to properly describe the vibrations for both stationary and non-stationary conditions. The proposed model provides the theoretical source and reliance for the order spectrum analysis and the diagnostic mechanism of the OER. Second, the OER is theoretically proved to be a reliable fault indicator by analyzing the proposed signal model. The offset of the uncertainty terms highlighting the fault symptoms demonstrates the potential success of using OER for the fault diagnosis. Finally, OERs generated from the vibration data and three typical machine learning algorithms are used to adaptively obtain the optimal bandwidth and thus the AOER. The AOER can be directly used for the remaining vibration data of the corresponding operational condition. Comparing with SER and the convolutional neural network, the experimental results demonstrate the effectiveness and outperformance of AOER for both stationary and non-stationary operational conditions. More importantly, since AOER can cope with diagnostic tasks for different operational conditions, it enables an improved ability to monitor a PG’s health in the actual complex environment.

Software Sensors for Order Tracking Applied to Permanent Magnet Synchronous Generator Diagnostics: A Comparative Study

The paper deals with software sensors which facilitates the diagnosis of electrical machines in non-stationary operating conditions. The technique targeted is order tracking for which different techniques exist to estimate the speed and angle of rotation. However, from a methodological point of view, this paper offers a comparison of several methods in order to evaluate their performance from tests on a test bench. In addition, to perform the tests, it is necessary to initialize the different methods to make them work correctly. In particular, an identification technique is proposed as well as a way to facilitate initialization. The example of this paper is that of a synchronous generator. Angular sampling allows the spectrum to be stationary and the interpretation of a possible defect. The realization of the angular sampling and the first diagnostic elements require the knowledge of two fundamental quantities: the speed of rotation and the angular position of the shaft. The estimation of the rotation speed as well as the estimation of the angular position of the shaft are carried out from the measurement of an electric current (or three electric currents and three voltages). Four methods are proposed and evaluated to realize software sensors: identification technique, PLL (Phase Locked Loop), Concordia transform and an observer. The four methods are evaluated on measurements carried out on a test bench. The results are discussed from the diagnosis of a mechanical fault.

Damage Diagnosis of Cardan Shafts in Mobile Mining Machines using Vibration Analysis

The ability of prediction of machine condition may become even more crucial in mobile mining machines considering their instantly-varying conditions. The field of early damage diagnosis for mobile machinery is gaining increasing attention in modern and predictive maintenance strategies. In general, vibration-based machine condition monitoring methods offer the possibility to detect and localize the damages in mechanical systems at their early stages. In vibration monitoring, the vibration signals are captured via accelerometers, and are processed in the time and frequency domains in signal processing packages to extract diagnostic information. However, in real-world mobile applications, vibration analysis alone may not reveal the diagnostic information due to extreme transient and nonstationary operating conditions. In this regard, additional metadata available on the machine’s CAN bus system, can improve the reliability of vibration analysis for mobile applications. The current study aims to investigate the usage of metadata as additional information to the vibration analysis on cardan shaft of a test wheel loader in field conditions. Two various damage types, namely, misalignment and bearing clearance, both on cardan shaft, are investigated in some detail. The experimental results show how vibration analysis together with metadata processing can identify the state of the machine even in harsh operating conditions.

Diagnosis of Inter-Turn Faults Based on Fault Harmonic Component Tracking in LSPMSMs Working Under Nonstationary Conditions

Inter-turn faults are one of the most important issues in line-start permanent magnet synchronous motors (LSPMSMs). To date, steady-state operating conditions of LSPMSMs have been analyzed in studies focusing on diagnosing this fault. However, no studies have been conducted for nonstationary operating conditions, such as variable speed or variable load conditions, which are common in the industry. This paper presents a novel approach for fault harmonic component (FHC) tracking based on transient-MCSA to diagnose inter-turn faults in an LSPMSM operating under nonstationary conditions. First, the inter-turn failure model of LSPMS was analyzed to show the fault effect on motor current, and the most dominant inter-turn FHC in the order and frequency domains were determined. Then, using Gabor-OT, inter-turn FHC signals were extracted from motor current signals in the frequency domain and reconstructed in the time domain. Intrinsic mode functions (IMFs) were calculated by decomposing the reconstructed FHC signals (RFHCSs) via ensemble empirical mode decomposition. Using the energies of RFHCSs, calculated based on Gabor coefficients, and the Kullback–Leibler divergences of the selected IMFs, diagnosis of the inter-turn faults and detection of the severity of the faults was performed. The results are in agreement with the results in the literature, thus showing that the proposed method is a successful and useful means for detecting inter-turn faults in LSPMSMs.

Operation Efficiency Optimization for Permanent Magnet Synchronous Motor Based on Improved Particle Swarm Optimization

In this paper, an improved online particle swarm optimization (PSO) is proposed to optimize the traditional search controller for improving the operating efficiency of the permanent magnet synchronous motor (PMSM). This algorithm combines the advantages of the attraction and repulsion PSO and the distributed PSO that can help the search controller to find the optimal d - axis air gap current quickly and accurately under non-stationary operating conditions, thereby minimizing the air gap flux and then improving the motor efficiency. To verify the effectiveness and stability of this proposed algorithm, the operating efficiency of PMSM as using this proposed algorithm is compared with that of traditional search controller under non-stationary operating conditions. The results show that the proposed algorithm can improve the operating efficiency of PMSM by 6.03% on average under non-stationary operation conditions. This indicates that the search controller based on the improved PSO has a better adaptation to the variation of external operating conditions, and can improve the operation efficiency of PMSM under non-stationary condition.

An automatic speed adaption neural network model for planetary gearbox fault diagnosis

In real-world applications, fault detection and diagnosis of planetary gearboxes are vital if it can be employed to avert catastrophic failure consequences in rotating machinery. Fault diagnosis usually starts with collecting vibration signals from rotating machinery. These vibration signals are usually produced in non-stationary operating conditions with time-varying loads and speeds, which makes fault diagnosis more challenging. Signal processing methods are typically selected for fault diagnosis to capture either time, frequency, or time-frequency based diagnostic features from measured vibration signals. Yet, it is usually a costly or time-consuming process and, sometimes, heavily dependent on human expertise. Although current deep learning algorithms offer an efficient and intelligent diagnostic strategy for fault diagnosis, unfortunately, most of the reported algorithms are basically only valid for the stationary operating conditions. To address the challenges of non-stationary operating conditions, in this paper, an Automatic Speed Adaption Neural Network (ASANN) model within the incorporation of instantaneous rotating speed is proposed, and it provides an end-to-end learning fashion with the guidance of rotating speed information. With the incorporating of instantaneous rotating speed information, the proposed ASANN model enables the extraordinary capacity for planetary gearbox fault detection under varying operational scenarios. The validity of the ASANN model is verified by an experimental investigation of fault diagnosis in a planetary gearbox.

Angular-based modeling of unbalanced magnetic pull for analyzing the dynamical behavior of a 3-phase induction motor

A multiphysics asynchronous electric machine model based on the angular approach is developed in this paper by adding the interaction between the Unbalanced Magnetic Pull and the rotor center radial displacements to reinforce the electro-magneto-mechanic fields couplings. The novelty of the proposed model lies also in calculating the instantaneous angular speed of the shaft and therefore tackling problems in non-stationary operating conditions. This radial force is generated from the magnetic field distortion mainly due to the rotor eccentricity. The shifted rotor geometric center coordinates are introduced to calculate the effective air gap length in the case with eccentricity. The Permeance Network Method is adopted to describe the magnetic field distribution and the electromagnetic force are achieved by applying the virtual work method. This multiphysics model is validated in the quasi-static regime by comparing with the reference data from the traditional numerical model in the case with and without input eccentricity. The physical characteristics about Unbalanced Magnetic Pull and its frequency components are investigated in angular domain to interpret the transfer path from different angularly-periodic variation fields in the induction machine. The impact of the rotor eccentricity amplitude variation on the dynamic behavior of the electric machine are analyzed at different operating points.

An Improved Key-Phase-Free Blade Tip-Timing Technique for Nonstationary Test Conditions and Its Application on Large-Scale Centrifugal Compressor Blades

Large-scale centrifugal compressor is an essential fluid machinery in modern industry. The blades, a critical part of compressors, always work under severe and harsh conditions and are prone to fatigue failure. Thus, a suitable monitoring technique is necessary to guarantee reliable operation of blades in the long-term. One promising, noncontact, online, technique, is the blade tip-timing (BTT) which is able to recover the vibration characteristics of all the blades simultaneously. However, improvements are still necessary to ensure the accuracy and reliability of the BTT technique for vibration monitoring of large-scale centrifugal compressors. In this article, the resolution error induced by the finite, discrete sampling is studied first. Then, the experimental random errors brought by nonstationary operating conditions are analyzed in detail and the error introduced by the compensation of the geometrical imperfection of the blades is also taken into account. An improved technique is proposed to reduce such random errors by substituting the real key-phase signal with reconstruction by means of the tip-timing signal. Experiment on a centrifugal compressor test-rig with a cracked blade is conducted to verify the effectiveness of the improved technique. Finally, a dynamic model is built to support the experimental result. This work demonstrates the reliability of the improved BTT technique for blade condition monitoring and fault warning of large-scale centrifugal compressors.

Decision Tree-Based Classification for Planetary Gearboxes' Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space.

Monitoring the condition of rotating machinery, especially planetary gearboxes, is a challenging problem. In most of the available approaches, diagnostic procedures are related to advanced signal pre-processing/feature extraction methods or advanced data (features) analysis by using artificial intelligence. In this paper, the second approach is explored, so an application of decision trees for the classification of spectral-based 15D vectors of diagnostic data is proposed. The novelty of this paper is that by a combination of spectral analysis and the application of decision trees to a set of spectral features, we are able to take advantage of the multidimensionality of diagnostic data and classify/recognize the gearbox condition almost faultlessly even in non-stationary operating conditions. The diagnostics of time-varying systems are a complicated issue due to time-varying probability densities estimated for features. Using multidimensional data instead of an aggregated 1D feature, it is possible to improve the efficiency of diagnostics. It can be underlined that in comparison to previous work related to the same data, where the aggregated 1D variable was used, the efficiency of the proposed approach is around 99% (ca. 19% better). We tested several algorithms: classification and regression trees with the Gini index and entropy, as well as the random tree. We compare the obtained results with the K-nearest neighbors classification algorithm and meta-classifiers, namely: random forest and AdaBoost. As a result, we created the decision tree model with 99.74% classification accuracy on the test dataset.

Improved Envelope Spectrum via Feature Optimisation-gram (IESFOgram): A novel tool for rolling element bearing diagnostics under non-stationary operating conditions

Demodulation methods are widely used for bearing diagnostics, based often on the (Squared) Envelope Spectrum after band pass filtering. One of the main challenges of these methods is the detection of a suitable band for signal demodulation under varying speed conditions. Angular resampling methods may synchronize the impulsive nature of bearing damages with a certain periodicity, in the case of large speed fluctuations. On the other hand, the time-invariant carrier frequencies may spread over the broadband of the spectrum, making impossible the application of many band selection tools. Lately, focus has been targeted to cyclostationary-based tools, such as the Cyclic Spectral Correlation and the Cyclic Spectral Coherence, which achieve a high performance in detecting hidden cyclic periodicities. Initially, these methods have been represented in the Frequency-Frequency domain, however they have been extended, to the Order-Order and the Order-Frequency domains, to be able to describe signals under varying speed conditions. The integration of these bi-variable maps over a specific frequency band results to an equivalent to a demodulated spectrum. Therefore, the challenge is the selection of the proper integration band to obtain these demodulated spectra for bearing diagnostics. In this paper, a novel tool called the Improved Envelope Spectrum via Feature Optimization-gram (IESFOgram) is proposed as a band selection tool for the demodulation of the bi-variable map (CSC or CSCoh) for bearing diagnostics. The method is represented in a 1/3-binary tree and is applicable under constant and variable speed conditions. The methodology is tested and validated on real data captured on a laboratory planetary gearbox and on an aircraft engine gearbox, under both constant and varying speed conditions. Furthermore, the methodology is compared in terms of performance with the Fast Kurtogram and the Autogram-based methods.

Improving the monitoring indicators of a variable speed wind turbine using support vector regression

For over a decade, most wind turbines have worked by adapting their rotation speed to that of the wind. This operating method, now widely used, allows optimal tip speed ratio to be achieved whatever the weather conditions, and in fact produces much better output than stall controlled turbines, particularly in calm weather conditions. However, this improvement means that monitoring systems are required to adapt to constant macroscopic variations in load and speed. In addition, these non-stationary operating conditions make it difficult to undertake machine diagnostics over the long term, due to the fact that the operating conditions in which successive indicators are obtained will almost never be the same. The scientific community has, in many respects, proved the usefulness of regression analysis of these indicators in relation to properly selected variables. The focus of this paper is on regression methods based on machine learning tools, which are becoming more and more popular. The difficulty lies in designing a robust self-adaptive method for estimating the statistical behaviour of an indicator in relation to operating conditions. Indeed, the concern is that indicators may obey disparate and unpredictable multivariate laws: there are many complications which make it difficult to use linear regression tools. Kernel machines, used in this paper as a robust and efficient way of normalising indicators, have proved to be capable of greatly improving a monitoring system’s diagnostic capabilities. The demonstration is based on a practical example: monitoring a bearing defect by analysing the instantaneous angular speed of the wind turbine shaft line. As this defect can only be detected under certain operating conditions – a priori unknown – the chosen example will be particularly effective in highlighting the usefulness of such an approach.

Deep digital twins for detection, diagnostics and prognostics

A generic framework for prognostics and health monitoring (PHM) which is rapidly deployable to heterogeneous fleets of assets would allow for the automation of predictive maintenance scheduling directly from operational data. Deep learning based PHM implementations provide part of the solution, but their main benefits are lost when predictions still rely on historical failure data and case-by-case feature engineering. We propose a solution to these challenges in the form of a Deep Digital Twin (DDT). The DDT is constructed from deep generative models which learn the distribution of healthy data directly from operational data at the beginning of an asset’s life-cycle. As the DDT learns the distribution of healthy data it does not rely on historical failure data in order to produce an estimation of asset health. This article presents an overview of the DDT framework and investigates its performance on a number of datasets. Based on these investigations, it is demonstrated that the DDT is able to detect incipient faults, track asset degradation and differentiate between failure modes in both stationary and non-stationary operating conditions when trained on only healthy operating data.

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.

Metal degradation of thermally loaded elements of power plants during operation in the North

The study of structural and phase changes and processes of destruction of heat-resistant nickel alloys is necessary to solve urgent problems of improving the serviceability and reliability of parts of the hot path of gas turbine installations, including under severe temperature and power operating conditions in the North. The purpose of this work is to study the high-temperature degradation of metal and the nature of operational failures of parts of the hot path (heat insert, blades) of gas turbine units operating in an extremely cold climate. Using the methods of optical, scanning and transmission electron microscopy the morphological changes were revealed in the strengthening γ’-phase and other symptoms of instability in structural state of the alloys from which the failed parts had been made. The insufficient level of structural stability of alloys in non-stationary operating conditions of gas turbine installations in the North which sharply increase the load on the parts is revealed. The degradation of alloys was developed in the depletion of the solid solution by the intermetallide phase, the softening of intermetallide particles by internal defects, the development of intra-grain porosity, and the weakening of grain boundaries by precipitates and pores. The fracture of the heat insert (made of a deformable nickel alloy) was realized by the creep mechanism, the fracture of the working blades (made of a casting alloy) – by the thermal fatigue mechanism. A limited resource of the studied parts is caused by the insufficient level of service properties for the considered operating conditions as well as error modes of the gas turbines caused the overheating of parts. The improving of materials for gas turbine installations in the North requires the adapted optimization of alloys compositions and the development of technologies for manufacturing parts taking into account operating conditions.