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Faults are inevitable in building energy systems, such as heating, ventilation, air conditioning and refrigeration systems. With the increasing utilization of these systems, their fault detection and diagnosis has become more significant for maintaining their operation and performance. The rapid development of data analytics has led to fault detection and diagnosis using data-based algorithms considering building intelligence. Feature engineering, which focuses on generating near minimum and optimal model inputs from the original dataset, is a fundamental technique to balance efficiency and accuracy of fault detection and diagnosis models. The generated inputs are fault features which ensures a better description of fault operations owing to their stronger fault indicative capability than original data. However, since most feature engineering research has been implemented alongside the development of data-based algorithms, most studies fail to effectively distinguish and understand their roles in fault detection and diagnosis models. To fill this research gap, this study presented the problems and mechanisms associated with feature engineering, comprehensively reviewed about 200 papers on the topic of feature engineering for heating, ventilation, air conditioning and refrigeration system fault detection and diagnosis tasks over the past 15 years. Firstly, feature engineering studies are divided according to their feature-generation technique, into manual and automated techniques. Next, feature engineering studies are analysed in terms of data type, system type, fault type, operation statue, methods and fault features. The desirable characteristics of feature engineering works are then discussed in terms of their capability of rapidly generating indicative, decoupled, interpretable and new fault features to enhance fault detection and diagnosis accuracy; the efficiency, reliability and robustness of feature engineering process; the mutual adaptability between feature engineering and fault detection and diagnosis; and their widespread applicability for more fault types and system operations. The challenges affecting feature engineering studies are discussed in terms of data volume, data diversity and quality, along with the performance evaluation of feature engineering with respect to feature engineering algorithms, fault detection and diagnosis models, fault features and online application constraints. Future work should aim to design a hybrid, heuristic, robust and automated feature engineering strategy under the guidance of expert knowledge and practical application constraints that consider the relationships between fault-related features and real-time fault impacts with energy consumption, occupants’ thermal comfort, etc.

Automotive electrical equipment can be summarized into three major components: power supply system, electrical equipment, and intermediate devices. For any electrical equipment and electronic control device to obtain power supply, the connection of intermediate devices is essential. The generator is the main power source of the car, and its function is to supply power to all electrical equipment (except the starter) when the engine is running normally (above idle speed), and to charge the battery at the same time. In this paper, by modeling the circuit of low-voltage electrical system of trams and designing and realizing the circuit model of low-voltage electrical system, this article masters the basic knowledge of circuit analysis, is familiar with the general process of circuit design, and can solve the general circuit faults in debugging.

In heating, ventilating and air conditioning (HVAC) systems, sensor faults cause improper control strategy resulting in both energy penalty and service costs. As the first and foremost step of an entire on-line fault-tolerant control strategy, sensor fault detection is crucial for maintaining system operation performance. Principal component analysis (PCA) is a widely studied sensor fault detection method in HVAC area. However, the noise information contained in field sensor measurements may confuse the PCA model training process and deteriorate the sensor fault detection performance. To address the problem, this study presented an enhanced PCA-based sensor fault detection method using ensemble empirical mode decomposition (EEMD) denoising. The proposed EEMD-PCA method includes two steps: (1) EEMD is adopted to decompose and denoise the raw measured data, namely eliminating the possible noise information and extracting the possible useful information; (2) PCA is employed to establish the Q-statistic with threshold for sensor fault detection. A case study on a real screw chiller system was conducted to validate the proposed method. 11 sensors were chosen for modeling by qualifying chiller energy performance. Field operating data were used for validation with various magnitudes of added biases. Results revealed that EEMD-PCA showed better detection performance than PCA for 8 critical sensors. The fault detection ratios of the 8 sensors were increased by 22% at average. After pre-processed by the EEMD method, the denoised data were smoother than the raw data. Data distributions in the PCA residual subspace indicated that EEMD-PCA is sensitive to the smaller biases since the denoised data can separate faulty data from the normal data.

Space-based optical (SBO) sensor is a special kind of star sensor, which can be used in not only the space-based observation for space objects but also satellite navigation, called optical navigation in this paper. The aim of this paper is to design optical navigation strategy for low earth orbit (LEO) satellites, including the selection of beacon objects, and the sensor scheduling for improving navigation accuracy. The processing and observation geometry of space-based optical observation is illustrated first. Based on the characteristics of the space-based optical observation, the distribution of beacon geosynchronous orbit satellites is designed for optical navigation of LEO satellites. And the sensor scheduling criterion for optical navigation is deduced to analyze the characteristics of optical navigation and schedule the navigation mission. It is found that an efficient way to improve the optical navigation accuracy is to add an additional sensor to track other beacon objects, whose line of sight (LOS) should be perpendicular to the original LOS of the sensor as far as possible. Therefore, global positioning system satellites are used as the supplement of beacon objects. Finally, based on the sensor scheduling criterion, navigation for a LEO satellite with two SBO sensors is scheduled, which obtains more accurate results and converges more rapidly.

This paper presented a chiller sensor fault detection method based on Empirical Mode Decomposition (EMD) threshold denoising and Principal Component Analysis (EMD-TD-PCA). EMD-TD-PCA was developed to remove noise in the original data set so as to improve fault detection efficiency for temperature sensor bias faults. First, EMD was used to enhance the data quality by removing the noise contained in the raw data set. A threshold denoising way was employed since it only eliminated noise but maintained the useful information. Second, the data processed with EMD threshold denoising were used to build the PCA model. The Q-statistic was adopted to detect the sensor faults. The proposed method was compared with the traditional PCA method. The operational data of a screw chiller system in an electric factory were used to evaluate this method. Results show that the EMD-TD-PCA method can effectively improve the fault detection efficiency especially for temperature sensor faults with introduced biases ranging from −2°C to +2 °C at the 0.5 °C interval, and also there will be certain differences in fault detection efficiency for different sensors.

Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component analysis, its compression factor score contains more original data characteristic information, therefore, partial least squares have greater potential for fault diagnosis than the principal component analysis. However, there are few studies based on partial least squares in the field of HVAC. In order to introduce partial least squares into the field, based on the partial least squares fault detection theory, a fault analysis method suitable for this field is proposed, and the RP1403 data published by ASHARE was used to verify this method. The results show that on the basis of selecting the appropriate number of principal components, partial least squares have the ability to diagnose the fault of the chiller sensor. With the known fault source, partial least squares regression, a method with better data reconstruction accuracy than principal component analysis, is used to repair the fault. Finally, the purpose of fault identification can be achieved.

One of the most fundamental qualities for science research and technology development is the innovation quality, which covers many aspects such as consciousness, method and spirit. The innovation quality developed in the undergraduate period would be the most basic foundation in their over forty-years working life. A supplemental cultivation plan with a routine consisting of hardware, programming, simulation and optimization was presented in this paper for the undergraduates of Building Environment and Energy Engineering (BEEE). The hardware manufacture of digital devices was trained to promote the freshmen manipulative capability, while software programming with general-purpose languages was introduced to enhance the comprehension of data analysis in the 2nd academic year. During the period when some professional courses were taught, the building simulation modular could be introduced to enhance the understanding for the building thermal process and its corresponding Heating, Ventilating, Air-conditioning and Refrigeration (HVAC & R) system. A building management system (BMS) would be developed step by step and interactive with virtual building platform. That BMS and its virtual cases could be employed as the testbed for optimization of HVAC & R system, such as energy consumption prediction, fault detection and diagnosis. Python and EnergyPlus were used in the custom training program oriented to the employment direction for the system operation and maintenance. Group achievements and individual cases show that the presented supplemental cultivation plan played a very important role in the cultivation of BEEE graduates’ innovative quality. The effect for the graduated students need pay more attention to investigate the long-term effects, and more volunteers will participate in this plan to find out their better future.

Since the outdoor and indoor load conditions always change continuously, it may lead to the various operation characteristics in chillers. If a single principal component analysis (PCA) model is applied to detect and diagnose sensor faults under a wide range of chiller operation conditions, the various operation characteristics may confuse the sensor fault analysis process in two aspects. One is that the single PCA model may not be sensitive enough to detect less server faults due to the inadaptable and fixed fault detection boundary; the other is that the single PCA model can hardly distinguish some new normal operation conditions from serious sensor faults since it is impossible for a training PCA model to cover all chiller operation conditions. To overcome the limitations, this study proposed an improved sensor fault detection, diagnosis and estimation (FDD&E) method combining density-based clustering with PCA. Clustering analysis, i.e., the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) can automatically classify operation data into clusters and recognize the corresponding operation conditions. Instead of a single PCA model, using sub-PCA models to describe each normal operation condition improved the sensitivity and reliability of fault detection and diagnosis as well as the accuracy of sensor fault estimation. The proposed method was validated using field operation data of an existing screw chiller plant while various sensor faults of different magnitudes were introduced. Results reveal that the proposed method shows better sensor FDD&E results than conventional PCA-based sensor FDD&E method using a single PCA model.

Building heating/cooling consumption prediction is of great importance for HVAC system management tasks, such as optimal operation/control strategies, demand and supply management, abnormal energy diagnosis, etc. Compared to traditional methods, data-driven methods have received a lot of attention due to their flexibility and efficiency. In particular, this paper investigates the potential of data partitioning techniques in improving prediction performance of ultra-short-term building heating load prediction. Specifically, with three proposed statistical attributes of 32 days considered by clustering analysis, similar daily operation patterns of pumps (OPPs) in a water-source heat pump system (WSHPS) were identified stepwise. Afterward, the sub-models based on different OPPs were developed by machine learning methods and their performance were compared to the general model without data partitioning. In additional, an operation tree was constructed to predict daily OPPs based on historical weather conditions and available date information. With the assistance of the operation tree, the proposed method can be applied in online prediction. Based on the validation, it can be concluded that the introduction of OPPs-clustering can improve the performance of building heating load prediction.

This letter deals with blind spatial diversity equalization and combination of sparse channels. The equalization and combination problem of multiple signals is modeled as the maximum likelihood estimation of frequency domain symbol sequence from incomplete observations, and solved by means of the expectation-maximization (EM) algorithm. Closed-form expression of the equalization output is obtained, which shows that the complicated problem of signal equalization and combination in multipath channels is converted to the weighted summation of each discrete-frequency signals, eliminating the need of complicated maximum likelihood sequence estimation (MLSE) or de-convolution demanded by time domain equalization. Simulation results show that the proposed scheme enables evident performance improvement in terms of symbol error rate especially at low signal-to-noise ratio (SNR) values and short signal lengths compared with a typical scheme.

Autonomous absolute orbit determination (OD) for a satellite constellation using only inter-spacecraft measurements has considerable value in space systems engineering. This paper proposes a novel solution for cooperative autonomous OD for a system comprised of an arbitrary number of spacecraft, with inertial angles-only measurements. We establish a link between observability and a graph representation of measurements among spacecraft. For a system with a beacon or without beacons, it is shown that if a measurement graph topology has graph theoretic equivalence properties with a two-level tree form, the system is second-order and third-order locally weakly observable, respectively. To solve the distributed OD problem, the new interleaved distributed extended Kalman filter (IDEKF) is proposed. Several evaluation cases are designed to demonstrate the influence of measurement topology, number of spacecraft, and orbital configuration on the degree of system observability. Finally, the performance of the IDEKF estimation is illustrated for a ten-spacecraft constellation.

This letter deals with frequency domain equalization (FDE) in the presence of residual timing offsets. A novel iterative decision feedback equalizer for single-carrier FDE systems is proposed. Different from conventional schemes based on explicit point time-delay estimates, the proposed scheme operates directly on the oversampled matched filter output. For each iteration, FDE is performed first, and based on which, joint posterior distribution of the channel and timing parameters is obtained. A set of weights obtained from the corresponding posterior distribution, rather than explicit point time-delay estimate, are utilized for the time-delay compensation, eliminating the need of fine timing synchronization. Simulation results show that the proposed scheme enables evident performance improvement in terms of symbol error rate compared with conventional schemes under relative low sampling rate.

This paper presents an entire sensor fault detection and diagnosis (FDD) for screw chiller system using the support vector data description (SVDD) algorithm. It has advantages of solving problems on describing non-linear and non-Gaussian distributed data. A distance-based D-statistic plot is employed to detect sensor faults. Based on the distance transformation in mathematical way, a new distance variation-based DV-contribution plot is proposed to diagnose the sensor fault. The screw chiller field measured data is utilized to train the SVDD model via a hybrid parameter tuning approach combined the grid search and the 10-fold cross validation. Six typical sensor faults are introduced for validation, i.e. positive and negative biases, positive and negative drifts, precision degradation and complete failure. Test results of both D-statistic and DV-contribution plots show that the SVDD-based method has good FDD results for the six sensor faults. Furthermore, the proposed DV-contribution plot shows more accurate fault diagnosis results compared with the principal component analysis (PCA)-based Q-contribution plot.

This paper presents an algebraic solution of erroneous sensor's undetectable boundary to evaluate the sensitivity of chiller sensor fault detection based on principal component analysis. Q-statistic of PCA is normally applied as a collective statistical index to detect sensor fault by comparing its value with the threshold. However, Q-statistic has no specific physical meaning and cannot evaluate the sensitivity of the provided method for sensor fault detection. We analyzed the definition of Q-statistic and derived the numerical value of the minimum range not to detect sensor fault. Bias sensor fault of a fielded screw chiller was studied for each sensor in PCA model by introducing different severity levels. Results showed that each sensor has different fault detection sensitivity using the same PCA model. The undetectable boundary can be a criterion used to evaluate the detection sensitivity of PCA-based method easily.

Thermal discomfort is a widespread problem in the built environment, due in part to the variability of individual occupants' thermal preferences. Personal comfort systems (PCS) address this individual variability, and also enable more energy-efficient thermal conditioning in buildings by reducing the need for tight indoor temperature control. This study evaluates a novel approach to PCS that leverages the time-dependence of human thermal perception. A 6.25 cm2 wearable device, Embr Wave, delivers dynamic waveforms of cooling or warming to the inner wrist. In three thermal comfort tests conducted in a climate chamber with N = 49 subjects and temperatures between 20 and 28 °C, the device exhibited a corrective potential of 2.5 °C within 3 min for both warm and cool populations, while consuming ~1 W of power. The effect is even more pronounced (corrective potential up to 3.3 °C over periods of 3- and 45-min) when subjects are given control of the device's operation. Subjects are found to optimize the device settings for pleasantness, not for the intensity of sensation. These results indicate that this low-power, wearable device improves whole-body thermal sensation, comfort, and pleasantness. It is an appropriate tool for addressing the problem of thermal discomfort in moderate indoor environments.

China’s urban rail transit (URT) construction is coming into the stage of rapid development under the guidance of national policies. However, the URT construction projects belong to high-risk projects and construction safety accidents occur frequently. Presently, safety risk management is in continuous development. Unfortunately, due to risk data deficiencies and lack of relationship between participants and safety risk factors, most of the research results cannot be well applied to URT projects. To overcome the limits, this paper has applied the text mining method into safety risk analysis. Through word frequency analysis and cluster analysis, 15 safety risk factors and 3 participants are identified from 156 accident reports. In addition, the accident descriptive model has been established, which is composed of indirect safety risk factors (management defects), direct safety risk factors and participants. In this model, each accident is the standardized description of the corresponding accident information. This is useful for risk data accumulation and analysis. Then the network structure analysis and risk assessment methods are utilized to make clear 63 relationships among participants, management defects and direct safety risk factors. Subsequently, the risk value of each relationship is evaluated. These safety risk information is integrated into the accident descriptive model by using accident points. Finally, ABC analysis which is a popular and effective method used to classify items into specific categories that can be managed and controlled separately is used to analyze the safety risk management’s core process(A), important process(B) and general process(C) in the accident descriptive model. The research results show that the constructor should pay attention to construction coordination, safety specifications, safety measures and personnel education, the supervisor should attach importance to timely communication, the monitoring unit should pay attention to advanced forecast and dynamic control. The main research contributions are as follows: (1) A method of obtaining risk data from unstructured content has been provided; (2) The accident descriptive model could be utilized for risk data continuous accumulation; (3) The emphases of URT construction safety risk management are made clear.

The prevalence of camera-equipped smartphones and optoelectronic displays opens up a novel framework for wireless communication-optical camera communication. With displays as transmitters and cameras as receivers, a reliable optical communication link is established. In practice, the frame synchronization between transmitters and receivers is an inevitable challenge confronted in this new communication mode, whereas few previous studies are involved in this issue. Thus, we dedicate to model the frame synchronization issue, discuss the phenomenon that the receiver might lose frames or capture mixed frames, and propose frame loss detecting algorithm based on statistic characteristics of difference frames, while maintaining perceptual transparency. Our preliminary experiments have confirmed the validity of the theoretical analysis and the proposed algorithms.

This paper considers low-complexity transmit beamforming schemes for signal-to-interference-plus- noise ratio (SINR) balancing problem in the Gaussian multiple-input single-output multi-receiver wiretap channel (MISO-MRWC). The formulated max-min-fair problem are shown to be non-convex. To overcome the computational complexity, we develop the leakage-based and zero forcing (ZF) based beamforming algorithms for finding the local optimum transmit beamformers. Extensive simulation results illustrate that the proposed beamforming algorithms achieve low computational complexity without serious performance deterioration.

Intelligent security technology provides important clues and basis for case detection, however, the traditional intelligent security technology can not alarm a crime's happening in advance. By researching the relationship between criminal psychology and speech feature, we proposed a crime degree theory of internet speech which can alarm a crime's happening in advance by taking advantage of the internet speech. The theory which was based on criminal psychology, using multiple analytical methods such as machine learning and emotional dictionary, establishing the mathematical model and theoretical framework, gave the preliminary implementation method of the real system. Experimental result has shown that the pre-alarming system based on our theory has good pre-alarming capability against crime.

We investigate and analyze a multi-antenna iterative spectrum sensing algorithm based on the SUMPLE combining scheme. Benefitted from the SUMPLE weight calculation, a correlation-based iterative test statistics is proposed. Other than most of the spectrum sensing algorithms, the proposed one develops a SUMPLE-based iterative solution for signal detection. Through the proposed sensing algorithm, the simultaneously obtainment of combined signal with increased SNR as well as the presence of the primary users' signal can be achieved, which is of significant advantage for the following signal processing like demodulation. The simulation results suggest that the proposed algorithm is capable of achieving a better detection performance compared with a common multi-antenna energy detection algorithm.

Instead of using a single large aperture antenna to implement track and commute with spatial targets, array-based receiving architecture is quickly becoming an attractive approach due to its low cost, flexibility and robustness. In this paper, we consider a scene in which multiple receiving sensors are randomly distributed. Two co-channel signals from different sources with the same carrier frequency and modulation form are received by all the sensors simultaneously. Mutual interference inserted in the receivers makes it more intractable for both parameter estimation and symbol detection. A novel method in which estimation for unknown parameters like phase offsets and time delays are jointly processed among all the sensors is proposed. Two different detection mechanisms are analyzed and compared. Simulation results show that proposed algorithm outperforms the existing techniques in estimation accuracy and demodulation performance.

The thinking of signal-to-leakage-plus-noise ratio (SLNR) is widely used for multi-user multiple-input multiple-output (MU-MIMO) systems in many works. We analyze the secrecy performance of the SLNR-based beamforming scheme in the multiple-input single-output multi-antenna eavesdropper (MISOME) wiretap channel. Our analysis proofs the SLNR-based beamforming scheme has almost the same performance as the generalized eigenvalue decomposition (GED) based beamforming scheme, and has significant gain over the zero-forcing (ZF) beamforming scheme.

This paper focuses on carrier frequency recovery for BPSK burst transmissions over AWGN channel. Semi-data- aided (SDA) nonlinear frequency estimation algorithms based on maximum-likelihood (ML) principle are derived. According to the algorithms, fewer pilot symbols and some unknown data symbols can be used together for frequency estimation in order that both estimation accuracy and spectral efficiency can be satisfied simultaneously. Simulations are employed to assess the performance of the algorithms. The corresponding Cramer-Rao bounds are discussed. The theoretical analysis of the threshold effect of one estimator is also presented and compared with simulation results.

This paper deals with frequency estimation for QPSK burst-mode transmissions in AWGN channel. A semi- data-aided (SDA) feedforward estimator for estimation of the carrier frequency offset is proposed. According to the algorithm, known pilot symbols and unknown data symbols are used together in order that both estimation accuracy and spectral efficiency can be satisfied. The simulations show that the SDA estimator, which only use fewer pilot symbols, can provide a very good performance close to the CRLB with a large number of pilot symbols at moderately high signal-to-noise ratios.

In this paper, we devoted to semi-blind carrier frequency offset estimation for burst-mode transmission on the AWGN channel. Based on the framework of the well-known expectation-maximization (EM) algorithm, in addition to the uses of known pilot symbols, the proposed semi-blind estimation (SBE) algorithm jointly exploits the a posteriori information of the unknown data symbols which were referred to as the missing vector. Compared with the pilot-aided maximum-likelihood (ML) estimation, the performance of the estimation can be improved effectively without increasing the number of pilot symbols with the SBE algorithm. Simulation results are presented to verify the rationality of the SBE in terms of the mean square error (MSE), while it also shows that the performance can reach the CRLB at moderately high signal-to-noise ratios (SNR).