Minimum Quantization Research Articles

A hybrid method combining Lévy process and neural network for predicting remaining useful life of rotating machinery

The accurate prediction of remaining useful life (RUL) for rotating machinery with gears and bearings at its core plays a crucial role in ensuring equipment's safe operation and preventing catastrophic accidents. Therefore, this paper focuses on the RUL issue of rotating machinery, proposing a novel RUL prediction framework. Initially, leveraging multi-domain feature extraction and self-organizing map (SOM) networks, this paper constructs the dynamic entropy weighted minimum quantization error (DEWMQE) as the initial health indicator (HI). Subsequently, employing windowed inertia smoothing and scale correction mechanisms, this paper filters anomalies in HI, ensuring trend smoothing and scale consistency. Considering the degradation process and characteristics of rotating machinery systems, this study utilizes linear multi-fractional Lévy stable motion (LMLSM) to build a degradation model. Building upon this, to effectively utilize historical data and overcome the shortcomings of random process theory requiring predefined degradation paths, this study further integrates bidirectional gated recurrent units (BGRU) and similarity transfer learning methods to devise the BGTLLM hybrid model, enabling adaptive fitting of the degradation trend. Finally, the Monte Carlo (MC) method is employed to evaluate the uncertainty in RUL prediction. The effectiveness and accuracy of the proposed hybrid prediction model in RUL forecasting are validated using heavy-duty truck axle data and the PHM2012 dataset.

Construction of multi-features comprehensive indicator for machinery health state assessment

Health state assessment is critical for mechanical equipment’s smooth and healthy operation. This paper proposes a novel approach for health state assessment based on acoustic signals during the process of machinery running. It consists of multi-domain feature (MF) extraction and comprehensive health indicator (CHI) construction. MF is extracted from various acoustic features, including time and frequency (TF) features, mel-frequency cepstral coefficients, and gammatone frequency cepstral coefficients. The stacked long short-term memory (LSTM) is used to extract the high-level features of the MF, which are then input to the downstream PCA to obtain the LSTM-PCA health indicator (LP-HI). Parallelly, the MF is fed into the self-organizing mapping (SOM) model to calculate the minimum quantization error (MQE) as SOM-MQE health indicator (SM-HI). These two indicators are fused using weighted fusion and nonlinear mapping to calculate CHI. The experimental results on air compressor dataset show a 25.8% reduction in evaluation error compared with SOTA results in this paper. The proposed nonlinear mapping function furthermore reduces fitting error on HI by 38.9%. These demonstrate the effectiveness and superiority of the proposed method in machinery health state assessment.

Evaluation method for insulation degradation of power transformer windings based on incomplete internet of things sensing data

AbstractThis paper proposes a novel evaluation method to address the challenge of evaluating insulation degradation in power transformer windings based on incomplete online Internet of Things (IoT) sensing data. The method leverages the Wasserstein Slim Generative Adversarial Imputation Network with Gradient Penalty algorithm to fill the irregularly missing power transformer IoT perception data, including voltage, current, temperature, and partial discharge. Subsequently, electrical, thermal, and mechanical performance degradation damage indicators for transformer winding insulation are constructed using the filled and complete IoT perception data. By applying the tensor fusion algorithm, the characteristics of these degradation damage indicators are fused, leading to the development of a comprehensive degradation evaluation index for the winding insulation. The evaluation of the winding insulation degradation state is achieved through the minimum quantization error method. The proposed method is validated using the real‐world transformer IoT perception data, and the experimental results demonstrate its ability to accurately assess the degree of winding insulation degradation, regardless of the presence of random or continuous irregularities in IoT sensing data.

Dissipative control for switched nonlinear singular systems with dynamic quantization

This paper focuses on the state feedback control for discrete-time switched Takagi–Sugeno fuzzy singular systems with input quantization. The considered singular systems combine the characteristics of switched hybrid systems and Takagi–Sugeno (T–S) fuzzy systems. The main goal is to co-design the controller and the quantizer based on the interval adjustment rule and satisfy the prescribed dissipative performance such that the quantized closed-loop singular (QCLS) system is stochastically admissible. In addition, the design conditions of the minimum quantization range are obtained by optimizing related parameters. Finally, a tunnel diode circuit example is presented to demonstrate the effectiveness of the proposed new design method.

Photon Structure and Wave Function from the Vector Potential Quantization

A photon structure is advanced based on the experimental evidence and the vector potential quantization at a single photon level. It is shown that the photon is neither a point particle nor an infinite wave but behaves rather like a local “wave-corpuscle” extended over a wavelength, occupying a minimum quantization volume and guided by a non-local vector potential real wave function. The quantized vector potential oscillates over a wavelength with circular left or right polarization giving birth to orthogonal magnetic and electric fields whose amplitudes are proportional to the square of the frequency. The energy and momentum are carried by the local wave-corpuscle guided by the non-local vector potential wave function suitably normalized.

Fault Diagnosis Method for Bearing Based on Digital Twin

The bearing is an essential component of rotating machinery, as its reliability and running state have a direct impact on the machinery’s performance. Considering that deep learning-based fault diagnosis methods for bearing require a large amount of labelled sample data, a novel fault diagnosis framework based on digital twin is proposed. In the case of fault data available, self-organizing maps with minimum quantization error and support vector machine are employed to analyze the data. Where fault data is unavailable, a bearing digital twin model is first constructed to simulate the data, and the convolutional neural network combined with transfer learning is utilized to diagnose the bearing faults. Then, the law of bearing performance degradation is investigated. The effectiveness of the proposed method is verified using bearing vibration data.

Collaborative 3D face alignment and head pose estimation with frontal face constraint based on RGB and sparse depth

A collaborative multi-task learning framework is proposed for integrating 3D face alignment and head pose estimation using RGB and sparse depth with a frontal face constraint. Most previous works involve using dense depth images to exploit spatial information, but the acquisition of high-resolution dense depth images is not always available due to the limitation of the image capture device. Besides, the existing multi-task learning methods have the problem of insufficient learning since they have not bridged the semantic gap between both tasks. In this letter, integrating 3D face alignment and head pose estimation into a collaborative multi-task learning framework is proposed, which is supervised by a frontal face constraint. In addition, sparse depth is incorporated into the network to provide additional facial geometrical information. A UV position map is generated to conduct face alignment, and a head pose vector is output to predict the head orientation. The performance on AFLW2000-3D dataset is evaluated, and the minimum quantization errors are achieved for both tasks when compared with state-of-the-art methods, demonstrating the superiority of the proposed method.

Intelligent fault warning method of rotating machinery with intraclass and interclass infographic embedding

Rotating machinery is widely used in industrial production facilities, and once a failure occurs, it can be catastrophic. Alerting to potential defects in time to prevent further equipment degradation is a challenging task. In this paper, a novel two-stage fault warning framework is proposed for early fault warning of rotating machinery. Specifically, a new method based on intra-class and inter-class neighborhood information graph embedding orthogonal discriminant projection is firstly adopted in this framework to extract the global distribution feature information and local geometric structure information of the data so that the homogeneous distance is compressed and the heterogeneous distance is distanced. Secondly, the minimum quantization error between the sample to be measured and the optimal winning neuron weight vector is calculated by self-organizing map to characterize the health state change, and combined with the Beta distribution self-learning technique to establish the fault warning threshold to circumvent the defects brought by the traditional fixation and it. Finally, the effectiveness of the proposed method is verified in the bearing and planetary gearbox test cases, and exciting conclusions are obtained under different working conditions in the gearbox case.

Piecewise Parabolic Approximate Computation Based on an Error-Flattened Segmenter and a Novel Quantizer

This paper proposes a novel Piecewise Parabolic Approximate Computation method for hardware function evaluation, which mainly incorporates an error-flattened segmenter and an implementation quantizer. Under a required software maximum absolute error (MAE), the segmenter adaptively selects a minimum number of parabolas to approximate the objective function. By completely imitating the circuit’s behavior before actual implementation, the quantizer calculates the minimum quantization bit width to ensure a non-redundant fixed-point hardware architecture with an MAE of 1 unit of least precision (ulp), eliminating the iterative design time for the circuits. The method causes the number of segments to reach the theoretical limit, and has great advantages in the number of segments and the size of the look-up table (LUT). To prove the superiority of the proposed method, six common functions were implemented by the proposed method under TSMC-90 nm technology. Compared to the state-of-the-art piecewise quadratic approximation methods, the proposed method has advantages in the area with roughly the same delay. Furthermore, a unified function-evaluation unit was also implemented under TSMC-90 nm technology.

Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images.

The lung organ of human anatomy captured by a medical device reveals inhalation and exhalation information for treatment and monitoring. Given a large number of slices covering an area of the lung, we have a set of three-dimensional lung data. And then, by combining additionally with breath-hold measurements, we have a dataset of multigroup CT images (called 4DCT image set) that could show the lung motion and deformation over time. Up to now, it has still been a challenging problem to model a respiratory signal representing patients' breathing motion as well as simulating inhalation and exhalation process from 4DCT lung images because of its complexity. In this paper, we propose a promising hybrid approach incorporating the local binary pattern (LBP) histogram with entropy comparison to register the lung images. The segmentation process of the left and right lung is completely overcome by the minimum variance quantization and within class variance techniques which help the registration stage. The experiments are conducted on the 4DCT deformable image registration (DIR) public database giving us the overall evaluation on each stage: segmentation, registration, and modeling, to validate the effectiveness of the approach.

Image Retrieval using Hybrid Order Dither Block Truncation Code

A new approach is proposed to index images in database using features generated from the HODBTC compressed data stream. This indexing technique can be extended for CBIR. HODBTC compresses an image into a set of color quantizers and a bitmap image. The proposed image retrieval system generates two image features namely CCF and BPF from the minimum quantizer, maximum quantizer and bitmap image respectively by involving the visual codebook.

Particle Swarm Optimization-Based Data Aggregation in Wireless Sensor Network

Wireless sensor networks have battery-operated sensor nodes, which need to be conserved to have prolonged network lifetime. The amount of power consumed for routing sensed data from the sensor node to the sink node is large. Thus, in order to optimize the energy usage in sensor network efficient data aggregation techniques are needed. Particle swarm optimization (PSO) is a speculative and evolutionary computing technique based on swarm intelligence for solving optimization problems in sensor network such as nodes deployment, node scheduling, data clustering, and aggregation. The paper proposes a PSO-based sensor network aggregation protocol (PSO-SNAP) with K-means to provide initial centroid. The PSO has been used to find the optimal aggregated value having minimum quantization error. The output of the K-means algorithm is used as an initial centroid in PSO. Apart from K-means, K-medoid and simple average has also been used to provide initial seed to the PSO algorithm and results of all three approaches are compared.

An Integrated Framework of Drivetrain Degradation Assessment and Fault Localization for Offshore Wind Turbines

As wind energy proliferates in onshore and offshore applications, it has become significantly important to predict wind turbine downtime and maintain operation uptime to ensure maximal yield. Two types of data systems have been widely adopted for monitoring turbine health condition: supervisory control and data acquisition (SCADA) and condition monitoring system (CMS). Provided that research and development have focused on advancing analytical techniques based on these systems independently, an intelligent model that associates information from both systems is necessary and beneficial. In this paper, a systematic framework is designed to integrate CMS and SCADA data and assess drivetrain degradation over itslifecycle. Information reference and advanced feature extraction techniques are employed to procure heterogeneous health indicators. A pattern recognition algorithm is used to model baseline behavior and measure deviation of current behavior, where a Self-organizing Map (SOM) and minimum quantization error (MQE) method is selected to achieve degradation assessment. Eventually, the computation and ranking of component contribution to the detected degradation offers component-level fault localization. When validated and automated by various applications, the approach is able to incorporate diverse data resources and output actionable information to advise predictive maintenance with precise fault information. Theapproach is validated on a 3 MW offshore turbine, where an incipient fault is detected well before existing system shuts down the unit. A radar chart is used to illustrate the fault localization result.

Sparse auto-encoder with regularization method for health indicator construction and remaining useful life prediction of rolling bearing

Remaining useful life (RUL) prediction, allowing for mechanical predictive maintenance, reduces unplanned and expensive maintenance greatly. One of the great challenges of data-driven RUL prediction is to extract the features that describe the actual degradation process. This paper presents a health indicator (HI) construction method based on a sparse auto-encoder with regularization (SAEwR) model for rolling bearings. This paper includes two modules, HI construction and RUL prediction. In the stage of the HI construction, the original features are compressed and extracted by the SAEwR model. The extracted features are sorted according to the trendability, and the features with large trendability are selected to construct the HI by using minimum quantization error. In the module of RUL prediction, the maximum likelihood estimation method is used to estimate the parameters of the prediction model, and a particle filter-based RUL prediction with degradation model is proposed. The proposed method is benchmarked with variational auto-encoder, auto-encoder methods and principal component analysis. The data from PRONOSTIA and ABLT-1A platform support the value of our approach.

Analysis and modeling of quantization error in spike-frequency-based image sensor

A comprehensive analysis of the quantization error in spike-frequency-based image sensor is presented. The image sensor generates different frequency spikes under various light. Images can be reconstructed from spikes by counting the spike number in an integration period, however, this method will generate quantization error. Firstly, the quantization errors introduced by nonlinearity, comparator operation delay and synchronous reading mechanism, are studied. Subsequently, a previous designed spike-frequency-based image sensor with a 400(H) × 250(V) pixel array is tested, and the test results match well with the model results. When the illumination increases from 100 lx to 600 lx, the quantization error increases from −2.5% to 8% at the conditions that reset voltage is 3.3 V, reference voltage is 2.2 V, reading period is 25 μs and the integral period is 10 ms. In addition, under the conditions of illumination is 300 lx, reading period is 25 μs and integration period is 20 ms, the minimum quantization error (6.5%) can be obtained by selecting reset voltage is 2.9 V, reference voltage is 2.32 V. Thus, the results can serve design or usage guide for suppressing the quantization error of the spike-frequency-based image sensor.

Exploiting Surroundedness and Superpixel cues for salient region detection

In this paper, we will present a new salient region detection method by exploiting its surrounding and superpixel cues. Its main highlights are: 1) An input image is quantized to 256 colors by using minimum variance quantization; 2) Saliency maps is computed based on the figure-ground segregation of the quantized image; 3) Mean saliency value of each superpixel is employed to refine saliency maps further. This can highlight salient objects robustly and suppress backgrounds evenly. Experimental results show that the proposed method produces more accurate saliency maps and performs well against twenty-one saliency models concerning three evaluation metrics on two public datasets.

A hybrid DBN-SOM-PF-based prognostic approach of remaining useful life for wind turbine gearbox

Gearbox is one of critical transmission components in wind turbine (WT) having a high downtime rate among all subcomponents. Fault prognostics and health management (PHM) of WT gearbox is crucial to their high reliability operation. However, presence of background noise in WT signals restricts the applicability of existing PHM approaches in feature extraction. To solve this problem, a novel performance degradation assessment method based on deep belief network (DBN) and self-organizing map (SOM) is proposed to de-noise and merge multi-sensor vibration signals. Minimum quantization error (MQE) is defined as health indicator to detect incipient fault of WT gearbox. After health indicator construction, an improved particle filtering (PF) optimized by fruit fly optimization algorithm (FOA) is employed to predict the remaining use life (RUL) of WT gearbox. To take advantage of dynamic and random operation process of WT gearbox, Wiener-process-based degradation model is developed to improving RUL prediction efficiency. The effectiveness is validated by using simulated as well as experimental vibration signals obtained through a WT gearbox highly accelerated life test. The results illustrate that proposed method can evaluate performance degradation process and predict RUL of WT gearbox effectively.

Degradation assessment of ball bearings utilizing curvilinear component analysis

The performance degradation assessment of ball bearings is of great importance to increase the efficiency and the reliability of rotating mechanical systems. The large dimensionality of feature space introduces a lot of noise and buries the potential information about faults hidden in the feature data. This paper proposes a novel health assessment method facilitated with two compatible methods, namely curvilinear component analysis and self-organizing map network. The novelty lies in the implementation of a vector quantization approach for the sub-manifolds in the feature space and to extract the fault signatures through nonlinear mapping technique. Curvilinear component analysis is a nonlinear mapping tool that can effectively represent the average manifold of the highly folded information and further preserves the local topology of the data. To answer the complications and to accomplish reliability and accuracy in bearing performance degradation assessment, the work is carried out with following steps; first, ensemble empirical mode decomposition is used to decompose the vibration signals into useful intrinsic mode functions; second, two fault features i.e. singular values and energy entropies are extracted from the envelopes of the intrinsic mode function signals; third, the extracted feature vectors under healthy conditions, further reduced with curvilinear component analysis are used to train the self-organizing map model; finally, the reduced test feature vectors are supplied to the trained self-organizing map and the confidence value is obtained. The effectiveness of the proposed technique is validated on three run-to-failure test signals with the different type of defects. The results indicate that the proposed technique detects the weak degradation earlier than the widely used indicators such as root mean square, kurtosis, self-organizing map-based minimum quantization error, and minimum quantization error-based on the principal component analysis.

Automatic Control Systems for Adaptive Optical Systems: Analytical Review. Part 1: Adaptive Optical System Control of Onboard Laser Installations

The first part of the analytical review presents an introduction to automatic control systems (ACS) for the adaptive optical systems (AOS) and control of tip-tilt correctors to eliminate a laser beam jitter. Considers a composition and a purpose of the AOS basic components. Also gives the AOS schemes to be used to form the sharper object images and focus radiation on a target when propagating a laser beam in a turbulent atmosphere. Briefly discusses the general issues of the AOS control, namely single-channel and multichannel linear control, bandwidth limitations of the control system, and possible signal paths in ACS of AOS.The article in-detail describes a path of the harmonic signal through the units of the feedback control loop as applied to the plane mirror of a two-channel corrector of the wave front tip-tilt. Provides guidelines to select the minimum quantization time for a propagating digital signal.Considers the certain problems of constructing ACS to be applied to AOS of the on-board laser installations. A simulated installation model where light passes through a turbulent atmosphere allowed us to develop a linear quadratic Gaussian controller (LQG-controller). Using this controller the optimal control (i.e. minimizing the dispersion of the output signal measured) with good robustness of the tip-tilt corrector is carried out.The concluding part of the review presents the certain research results of the AOS control when compensating the laser radiation wave front perturbations caused both by an aero-optical problem, arising when radiation propagates near the walls of an aircraft and by an atmospheric turbulence of free airflow. The influence of a small time delay (within one sampling step), when transmitting a control signal, on the control system operability was under special consideration.

Improving the efficacy of clustering by using far enhanced clustering algorithm

There are several aspects on which research works are carried out on clustering. The prime focus is on finding the near optimal cluster centres and determining the best possible clusters. Hence, we have emphasised our work on finding a technique which contemplates on these facets in a way which is far more efficient than several novel approaches. In this paper, we have examined four varieties of clustering algorithms namely; K-Means, FEKM, ECM and proposed FECA implemented on varying data sets. We used few internal cluster validity indices like Dunn's index, Davies-Bouldin's index, Silhouette Coefficient, C index and Calinski index for quantitative evaluation of the clustering results obtained. The results obtained from simulation were compared, and as per our expectation it was found that, the quality of clustering produced by FECA is far more satisfactory than the others. Almost every value of validity indices used give encouraging results for FECA, implying good cluster formation. Further experiments support that the proposed algorithm also produces minimum quantisation error almost for all the data sets used.