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Local natural gas distribution companies (LDCs) require accurate demand forecasts across various time periods, geographic regions, and customer class hierarchies. Achieving coherent forecasts across these hierarchies is challenging but crucial for optimal decision making, resource allocation, and operational efficiency. This work introduces a method that structures the gas distribution system into cross-temporal hierarchies to produce accurate and coherent forecasts. We apply our method to a case study involving three operational regions, forecasting at different geographical levels and analyzing both hourly and daily frequencies. Trained on five years of data and tested on one year, our model achieves a 10% reduction in hourly mean absolute scaled error and a 3% reduction in daily mean absolute scaled error. [ABSTRACT FROM AUTHOR]

The benefits and drawbacks of a 5-hp reconfigurable induction motor, which was designed for experimental emulation of stator winding interturn and broken rotor bar faults, are presented in this paper. It was perceived that this motor had the potential of quick and easy reconfiguration to produce the desired stator and rotor faults in a variety of different fault combinations. Hence, this motor was anticipated to make a useful test bed for evaluation of the efficacy of existing and new motor fault diagnostics techniques and not the study of insulation failure mechanisms. Accordingly, it was anticipated that this reconfigurable motor would eliminate the need to permanently destroy machine components such as stator windings or rotor bars when acquiring data from a faulty machine for fault diagnostic purposes. Experimental results under healthy and various faulty conditions are presented in this paper, including issues associated with rotor bar-end ring contact resistances that showed the drawbacks of this motor in so far as emulation of rotor bar breakages. However, emulation of stator-turn fault scenarios was successfully accomplished. Index Terms--Condition monitoring, fault diagnostics, fault emulation, induction motors, reconfigurable motors, rotor broken bar faults, stator winding interturn faults.

A new method for the fault diagnosis of a broken rotor bar and interturn short circuits in induction machines (IMs) is presented. The method is based on the analysis of the three-phase stator current envelopes of IMs using reconstructed phase space transforms. The signatures of each type of fault are created from the three-phase current envelope of each fault. The resulting fault signatures for the new so-called 'unseen signals' are classified using Gaussian mixture models and a Bayesian maximum likelihood classifier. The presented method yields a high degree of accuracy in fault identification as evidenced by the given experimental results, which validate this method. Index Terms--AC motor drive systems, broken bars, envelope detection and classification, fault diagnosis, induction machines (IMs), induction motors, interturn short circuits.

A robust interturn fault diagnostic approach based on the concept of magnetic field pendulous oscillation, which occurs in induction motors under faulty conditions, is introduced in this paper. This approach enables one to distinguish and classify an unbalanced voltage power supply and machine manufacturing/construction imperfections from an interturn fault. The experimental results for the two case studies of a set of 5-hp and 2-hp induction motors verify the validity of the proposed approach. Moreover, it can be concluded from the experimental results that if the circulating current level in the shorted loop increases beyond the phase current level, an interturn fault can be easily detected using the proposed approach even in the presence of the existence of motor manufacturing imperfection effects. Index Terms--Broken-bar, condition monitoring, induction motor, interturn short circuit, stator fault, transient modeling.

A novel approach to the analysis and classification of time series signals using statistical models of reconstructed phase spaces is introduced. This is shown to be viable and effective alternative to traditional signal classification approaches, particularly for signals with strong nonlinear characteristics.

A new signal classification approach is presented that is based upon modeling the dynamics of a system as they are captured in a reconstructed phase space. The modeling is done using full covariance Gaussian Mixture Models of time domain signatures, in contrast with current and previous work in signal classification that is typically focused on either linear systems analysis using frequency content or simple nonlinear machine learning models such as artificial neural networks. The proposed approach has strong theoretical foundations based on dynamical systems and topological theorems, resulting in a signal reconstruction, which is asymptotically guaranteed to be a complete representation of the underlying system, given properly chosen parameters. The algorithm automatically calculates these parameters to form appropriate reconstructed phase spaces, requiring only the number of mixtures, the signals, and their class labels as input. Three separate data sets are used for validation, including motor current simulations, electrocardiogram recordings, and speech waveforms. The results show that the proposed method is robust across these diverse domains, significantly outperforming the time delay neural network used as a baseline. Index Terms--Signal classification, reconstructed phase spaces, Gaussian mixture models.

This paper develops the foundations of a technique for detection and categorization of dynamic/static eccentricities and bar/end-ring connector breakages in squirrel-cage induction motors that is not based on the traditional Fourier transform frequency-domain spectral analysis concepts. Hence, this approach can distinguish between the 'fault signatures' of each of the following faults: eccentricities, broken bars, and broken end-ring connectors in such induction motors. Furthermore, the techniques presented here can extensively and economically predict and characterize faults from the induction machine adjustable-speed drive design data without the need to have had actual fault data from field experience. This is done through the development of dual-track studies of fault simulations and, hence, simulated fault signature data. These studies are performed using our proven Time-Stepping Coupled Finite-Element-State-Space method to generate fault case performance data, which contain phase current waveforms and time-domain torque profiles. Then, from this data, the fault cases are classified by their inherent characteristics, so-called 'signatures' or 'fingerprints.' These fault signatures are extracted or 'mined' here from the fault case data using our novel Time-Series Data Mining technique. The dual track of generating fault data and mining fault signatures was tested here on dynamic and static eccentricities of 10% and 30% of air-gap height as well as cases of one, three, six, and nine broken bars and three, six, and nine broken end-ring connectors. These cases were studied for proof of principle in a 208-V 60-Hz four-pole 1.2-hp squirrel-cage three-phase induction motor. The paper presents faulty and healthy performance characteristics and their corresponding so-called phase space diagnoses that show distinct fault signatures of each of the above-mentioned motor faults. Index Terms--Artificial intelligence, data mining, diagnostics through current waveforms, dynamical systems analysis, electric drives, fault diagnosis, induction motors, state-space methods, time series, time-stepping finite elements.

A new method for analyzing time series data is introduced in this paper. Inspired by data mining, the new method employs time-delayed embedding and identifies temporal patterns in the resulting phase spaces. An optimization method is applied to search the phase spaces for optimal heterogeneous temporal pattern clusters that reveal hidden temporal patterns, which are characteristic and predictive of time series events. The fundemantal concepts and framework of the method are explained in detail. The method is then applied to the characterization and prediction, with a high degree of accuracy, of the release of metal droplets from a welder. The results of the method are compared to those from a Time Delay Neural Network and the C4.5 decision tree algorithm. Index Terms--Temporal pattern identification, time series analysis, data mining, time delay embedding, optimization clustering, and genetic algorithms.

This paper develops the fundamental foundations of a technique for detection of faults in induction motors that is not based on the traditional Fourier transform frequency domain approach. The technique can extensively and economically characterize and predict faults from the induction machine adjustable speed drive design data. This is done through the development of dual-track proof-of-principle studies of fault simulation and identification. These studies are performed using our proven Time Stepping Coupled Finite Element-State Space method to generate fault case data. Then, the fault cases are classified by their inherent characteristics, so-called 'signatures' or 'fingerprints.' These fault signatures are extracted or mined here from the fault case data using our novel Time Series Data Mining technique. The dual-track of generating fault data and mining fault signatures was tested here on three, six, and nine broken bar and broken end-ring connectors in a 208-volt, 60-Hz, 4-pole, 1.2-hp, squirrel cage 3-phase induction motor. Index Terms--Artificial intelligence, data mining, diagnostics through torque profiles, dynamical systems analysis, electric drives, fault diagnosis, induction motors, state space methods, time series, time stepping finite elements.

We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter. [ABSTRACT FROM AUTHOR]

In this paper, a new method for statistical estimation of Mel-frequency cepstral coefficients (MFCCs) in noisy speech signals is proposed. Previous research has shown that model-based feature domain enhancement of speech signals for use in robust speech recognition can improve recognition accuracy significantly. These methods, which typically work in the log spectral or cepstral domain, must face the high complexity of distortion models caused by the nonlinear interaction of speech and noise in these domains. In this paper, an additive cepstral distortion model (ACDM) is developed, and used with a minimum mean-squared error (MMSE) estimator for recovery of MFCC features corrupted by additive noise. The proposed ACDM-MMSE estimation algorithm is evaluated on the Aurora2 database, and is shown to provide significant improvement in word recognition accuracy over the baseline. [ABSTRACT FROM AUTHOR]