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Factor graph optimization (FGO) recently has attracted attention as an alternative to the extended Kalman filter (EKF) for GNSS-INS integration. This study evaluates both loosely and tightly coupled integrations of GNSS code pseudorange and INS measurements for real-time positioning, using both conventional EKF and FGO with a dataset collected in an urban canyon in Hong Kong. The FGO strength is analyzed by degenerating the FGO-based estimator into an “EKF-like estimator.” In addition, the effects of window size on FGO performance are evaluated by considering both the GNSS pseudorange error models and environmental conditions. We conclude that the conventional FGO outperforms the EKF because of the following two factors: (1) FGO uses multiple iterations during the estimation to achieve a robust estimation; and (2) FGO better explores the time correlation between the measurements and states, based on a batch of historical data, when the measurements do not follow the Gaussian noise assumption.

In this article, we proposed a graduated nonconvexity (GNC) aided outlier mitigation method for the improvement of the visual-inertial integrated navigation system (VINS) to face the challenge of dynamic environments with numerous unexpected outlier measurements. A GNC optical flow algorithm was proposed for the detection of the outliers of feature tracking in the front-end of VINS by iteratively estimating the optical flow and the optimal weightings of feature correspondences. Then the feature correspondences with small weightings were excluded. However, excessive outlier exclusion may cause insufficient constraints on the state, causing degeneration of VINS. To solve the problem, this article proposed to detect the potential degeneration based on the degree of constraint in different directions of the pose estimation. Then the number of features being considered was intelligently adapted based on the degeneration level to improve the geometry constraint in the coming epochs. We evaluated the effectiveness of the proposed method by using two challenging datasets (including challenging night scenarios) collected in urban canyons of Hong Kong. The results show that the proposed method can effectively reject the potential outlier visual measurements, and alleviate the degeneration, leading to improved positioning performance in both evaluated datasets.

Robust and globally-referenced positioning is indispensable for autonomous driving vehicles. Global navigation satellite system (GNSS) is still an irreplaceable sensor. Satisfactory accuracy (about 1 m) can be obtained in sparse areas. However, the GNSS positioning error can be up to 100 m in dense urban areas due to the multipath effects and non-line-of-sight (NLOS) receptions caused by reflection and blockage from buildings. NLOS is currently the dominant factor degrading the performance of GNSS positioning. Recently, the camera has been employed to detect the NLOS and then to exclude the NLOS measurements from GNSS calculation. The exclusion of NLOS measurements can cause severe distortion of satellite distribution, due to the excessive NLOS receptions in deep urban canyons. Correcting the NLOS receptions with the aid of 3D light detection and ranging after detection of NLOS receptions using a fish-eye camera was proposed in this study. Finally, the GNSS positioning was improved by using the healthy and corrected NLOS pseudo-range measurements. The proposed method is evaluated through real road tests in typical highly urbanised canyons of Hong Kong. The evaluation results show that the proposed method can effectively improve the positioning performance.

To monitor the voltage stability state of complex power grid, a four-category stability classification problem that incorporates a set of serious contingencies is posed. Quick decision-making and high accuracy are critical for the safety operation of power system. However, this problem involves feature of different types, levels and dimensions and is hard to be handled by the traditional classifier. This paper utilizes the deep learning technique and proposes a multi-level deep neural network (ML-DNN) that achieves feature fusion of the electrical parameter measurements, topology and contingency information. experiments are implemented on IEEE-39 system, the ML-DNN performs better in four main evaluation indices comparing with five existing models, which demonstrates its advantage for online voltage stability monitoring.

Achieving accurate and reliable positioning in dynamic urban scenarios using low-cost vehicular onboard sensors, such as the global navigation satellite systems (GNSS), camera, and inertial measurement unit (IMU), is still a challenging problem. Multi-Agent collaborative integration (MCI) opens a new window for achieving this goal, by sharing the sensor measurements between multiple agents to further improve the accuracy of respective positioning. One of the major difficulties in MCI is to effectively connect all the sensor measurements arising from multiple independent agents. The popular approach is to find the overlapping areas between agents using active sensors, such as cameras. However, the performance of overlapping area detection is significantly degraded in outdoor urban areas due to the challenges arising from numerous unexpected moving objects and unstable illumination conditions. To fill this gap, this paper proposes to leverage both the camera-based overlapping area detection and the inter-ranging measurements to boost the cross-connection between multi-agents and brings the MCI to outdoor urban scenarios using low-cost onboard sensors. Moreover, a novel MCI framework is proposed to integrate the sensor measurements from the low-cost GNSS receiver, camera, IMU, and inter-ranging using state-of-the-art factor graph optimization (FGO) to fully explore their complementary properties. The proposed MCI framework is validated using two challenging datasets collected in urban canyons of Hong Kong. We conclude that the proposed MCI framework can effectively improve the positioning accuracy of the respective agents in the evaluated datasets. We believe that the proposed MCI framework has the potential to be prevalently adopted by the connected intelligent transportation systems (ITS) applications to provide robust positioning using low-cost onboard sensors in urban scenarios.

Consistence of lithium-ion power battery significantly affects the life and safety of battery modules and packs. To improve the consistence, battery grouping is employed, assembling batteries with similar electrochemical characteristics to make up modules and packs. Therefore, grouping process boils down to unsupervised clustering problem. Current used grouping approaches include two aspects, static characteristics based and dynamic based. However, there are three problems. First, the common problem is under utilization of multi-source data. Second, for the static characteristics based, there is grouping failure over time. Third, for the dynamic characteristics based, there is high computational complexity. To solve these problems, we propose a distributed multisource data fusion based battery grouping approach. The proposed approach designs an effective network structure for multisource data fusing and feature extracting from both static and dynamic multisource data. We apply our approach on real battery modules and record state of health (SOH) during charging-discharging cycles. Experiments indicate that the proposed approach can increase SOH of modules by 3.89% and reduce the inconsistence by 68.4%. Meanwhile, with the distributed deployment the time cost is reduced by 87.9% than the centralized scheme.

To meet voltage and capability needs, batteries are grouped into packs as power sources. Abnormal ones in a pack will lead to partial heating and reduced available life, so removing anomalies out during manufacturing is of great significance. The conventional methods to detect abnormal batteries mainly rely on grading systems and manual operations. Current data-driven methods use statistical, machine learning and neural network approaches, building models, then applying them on the unlabeled. However, both cannot make full use of multiple source data and expert knowledge. Therefore, how to use these multi-source data and knowledge to improve the effect of battery anomaly detection process has become a research focus. We put forward a data-driven multi-source data feature fusion and expert knowledge integration (FFEKI) network architecture that follows encoder-decoder structure with multiple integration units and a corresponding joint loss function. First, we collect multi-source data and obtain fusion features. Then, we refine filters from expert knowledge and transform them into neural network layers as components of integration units. By this way, supervisory knowledge is integrated into our network. We evaluate our scheme by sets of experiments comparing with most widely used approaches on real manufacturing data. Results show that FFEKI obtains a maximum 100% anomaly detection rate (ADR). Meanwhile, when the number of detection T is greater than the actual number of anomalies in the testing set, our method can achieve full ADR faster. It is concluded that the proposed FFEKI achieves effective performance on power lithium-ion battery anomaly detection.

GNSS/INS integrated solution has been extensively studied over the past decades. However, its performance relies heavily on environmental conditions and sensor costs. The GNSS positioning can obtain satisfactory performance in the open area. Unfortunately, its accuracy can be severely degraded in a highly urbanized area, due to the notorious multipath effects and none-line-of-sight (NLOS) receptions. As a result, excessive GNSS outliers occur, which causes a huge error in GNSS/INS integration. This paper proposes to apply a fish-eye camera to capture the sky view image to further classify the NLOS and line-of-sight (LOS) measurements. In addition, the raw INS and GNSS measurements are tightly integrated using a state-of-the-art probabilistic factor graph model. Instead of excluding the NLOS receptions, this paper makes use of both the NLOS and LOS measurements by treating them with different weightings. Experiments conducted in typical urban canyons of Hong Kong showed that the proposed method could effectively mitigate the effects of GNSS outliers, and an improved accuracy of GNSS/INS integration was obtained, when compared with the conventional GNSS/INS integration.

The service life, safety, and capacity of lithium-ion power battery packs relies heavily on the consistency among battery cells. Grouping is an effective procedure to improve consistency by screening cells with similar performance and assembling them into an identical group. Battery grouping can be achieved via clustering techniques based on characteristics like static capacity, internal resistance etc. The dynamic characteristics-based method considers the battery performance during the entire charging-discharging process and has become one of the most promising grouping method. However, it suffers from high computational complexity. Nowadays, facing stricter quality standards and the increasing demand for battery products, existing dynamic characteristics-based grouping scheme cannot meet the performance and time requirement of the modern high-quality, large-scale battery manufacturing. In this paper, a novel grouping scheme based on distributed time-series clustering is proposed to match the need of both efficiency and consistency improvement. The proposed scheme designs an effective "cloud-edge" mode and utilizes an innovative two-stage trick to achieve parallel processing, which split the original centralized clustering approach into local clustering and global merging. The host computers for data acquisition and battery control are regarded as distributed edge computing resources to implement local clustering on the acquired battery discharging voltage sequence set. Cluster contours are extracted via a denoising contour extraction algorithm considering the irregularity of the discharging voltage sequences. The results of the above preliminary processing are uploaded to a cloud data center. A pragmatic merging scheme based on an integrated merging indicator is established to solve the decision-making problem of global merging on the cloud data center. The final global cluster set is transmitted back to host computers to instruct cell unloading operation. Experimental results based on real battery discharging voltage sequence data suggest that the proposed scheme can reduce the inconsistency rate by 43.56% and the time cost by 92.87%. The computing efficiency and resource utilization rate of the distributed scheme is much higher than the centralized scheme. Meanwhile, compared with three existing advanced grouping approaches, our scheme perform the best in reducing inconsistency rate. (C) 2019 Published by Elsevier Ltd.

Remaining useful life (RUL) prediction is critical for predictive maintenance of machinery. Data-driven prognostics methods centered on deep learning are attracting ever-increasing attention. However, most existing methods mainly provide point estimates about RUL without quantifying predictive uncertainty. In contrast, Bayesian models can offer a reliable framework for estimating predictive uncertainty, but these models require expensive computation cost. In this paper, we present a Bayesian framework based convolutional neural network (BCNN) that is easy to implement and can provide high-quality predictive uncertainty of RUL. The variational inference is adopted to approximate the posterior distribution over the model parameters. Then the approximating probability distribution is used for subsequent inference of newly observed data. The proposed method is validated using vibration signals obtained from the accelerated degradation of rolling element bearings. The time-frequency domain features are extracted from raw vibration signals using continuous wavelet transform. The results of the experiments show the effectiveness of the RUL prediction of machinery.

Mapping and localization is a critical module of autonomous driving, and significant achievements have been reached in this field. Beyond Global Navigation Satellite System (GNSS), research in point cloud registration, visual feature matching, and inertia navigation has greatly enhanced the accuracy and robustness of mapping and localization in different scenarios. However, highly urbanized scenes are still challenging: LIDAR- and camera-based methods perform poorly with numerous dynamic objects; the GNSS-based solutions experience signal loss and multipath problems; the inertia measurement units (IMU) suffer from drifting. Unfortunately, current public datasets either do not adequately address this urban challenge or do not provide enough sensor information related to mapping and localization. Here we present UrbanLoco: a mapping/localization dataset collected in highly-urbanized environments with a full sensor-suite. The dataset includes 13 trajectories collected in San Francisco and Hong Kong, covering a total length of over 40 kilometers. Our dataset includes a wide variety of urban terrains: urban canyons, bridges, tunnels, sharp turns, etc. More importantly, our dataset includes information from LIDAR, cameras, IMU, and GNSS receivers. Now the dataset is publicly available through the link in the footnote. Dataset Link: https://advdataset2019.wixsite.com/urbanloco., Comment: 7 pages, ICRA2020

Visual-inertial navigation systems (VINS) have been extensively studied in the past decades to provide positioning services for autonomous systems, such as autonomous driving vehicles (ADV) and unmanned aerial vehicles (UAV). Decent performance can be obtained by VINS in indoor scenarios with stable illumination and texture information. Unfortunately, applying the VINS in dynamic urban areas is still a challenging problem, due to the excessive dynamic objects which can significantly degrade the performance of VINS. Detecting and removing the features inside an image using the deep neural network (DNN) that belongs to unexpected objects, such as moving vehicles and pedestrians, is a straightforward idea to mitigate the impacts of dynamic objects on VINS. However, excessive exclusion of features can significantly distort the geometry distribution of visual features. Even worse, excessive removal can cause the unobservability of the system states. Instead of directly excluding the features that possibly belong to dynamic objects, this paper proposes to remodel the uncertainty of dynamic features. Then both the healthy and dynamic features are applied in the VINS. The experiment in a typical urban canyon is conducted to validate the performance of the proposed method. The result shows that the proposed method can effectively mitigate the impacts of the dynamic objects and improved accuracy is obtained.

Accurate and globally referenced positioning is crucial to autonomous systems with navigation requirements, such as unmanned aerial vehicles (UAV) and autonomous driving vehicles (ADV). GNSS/LiDAR integration is a popular sensor pair that can provide outstanding positioning performance in open areas. However, the accuracy is significantly degraded in urban canyons, due to the excessive unmodeled non-Gaussian GNSS outliers caused by multipath effects and none-line-of-sight (NLOS) receptions. As a result, the violation of the Gaussian assumption can severely distort the sensor fusion process, such as the extended Kalman filter (EKF). To mitigate the effects of these non-Gaussian GNSS outliers, this paper proposes to leverage the Gaussian mixture model (GMM) to describe the potential noise of GNSS positioning and apply it to further sensor fusion. Instead of relying on excessive offline parameterization and tuning, the parameters of the GMM are estimated simultaneously based on the residuals of the GNSS measurements using an expectation-maximization (EM) algorithm. Then the state-of-the-art factor graph optimization (FGO) is applied to integrate the GNSS positioning and LiDAR odometry based on the estimated GMM. The experiment in a typical urban canyon is conducted to validate the performance of the proposed method. The result shows that the GMM can effectively mitigate the effects of GNSS outliers and improves positioning performance.

The visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in deep urban areas, and these moving objects can severely distort the feature-tracking process which is critical to the feature-based VINS. One well-known method that mitigates the effects of dynamic objects is to detect vehicles using deep neural networks and remove the features belonging to surrounding vehicles. However, excessive feature exclusion can severely distort the geometry of feature distribution, leading to limited visual measurements. Instead of directly eliminating the features from dynamic objects, this study proposes to adopt the visual measurement model based on the quality of feature tracking to improve the performance of the VINS. First, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurement by integrating two parts: (1) the geometry of feature distribution (GFD); (2) the quality of feature tracking. Second, an adaptive M-estimator is proposed to correct the measurement residual model to further mitigate the effects of outlier measurements, like the dynamic features. Different from the conventional M-estimator, the proposed method effectively alleviates the reliance on the excessive parameterization of the M-estimator. Experiments were conducted in typical urban areas of Hong Kong with numerous dynamic objects. The results show that the proposed method could effectively mitigate the effects of dynamic objects and improved accuracy of the VINS is obtained when compared with the conventional VINS method.

In this letter we report a diamond lateral FinFET fabricated using an ohmic regrowth technique. The use of ohmic regrowth separates the source/drain and gate fabrication, providing a viable means to improve ohmic contact resistance while protecting the top surface of the diamond channel from dry etch damage. Enabled by high channel quality, the diamond transistor behavior was shown to transit from a pentode-like to a triode-like characteristic when channel length decreased. For the first time, space charge limited transport in diamond FinFETs with a short channel length was demonstrated. We have analyzed the space charge limited transport from room temperature to 150 °C. This space charge limited transport, in combination with improved ohmic contacts, will enable diamond FinFETs for various high-power applications.

Accurate situational assessment and severity rating are of great importance to the voltage stability of power grid. Traditional approaches depend heavily on the network parameters and component models, which restrict their applications. In this paper, an unsupervised situational assessment scheme is proposed to achieve a voltage stability margin-based, three-class situation categorization via the knowledge-aided siamese autoencoder and k-Means clustering. The distribution characteristic of voltage stability margin is utilized to provide support for searching optimal feature subspace that enables k-Means to minimize intra-class and maximize inter-class differences through the siamese architecture. Experiments on IEEE-39 system prove that the proposed scheme outperforms classical approaches in multiple indicators, which proves it a useful situational assessment tool for power grid voltage stability monitoring.

Neuromorphic networks of artificial neurons and synapses can solve computational hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, silicon neuromorphic processors outperform graphic processing units (GPUs) in energy efficiency by a large margin, but they deliver much lower chip-scale throughput. The performance-efficiency dilemma for silicon processors may not be overcome by Moore's law scaling of complementary metal-oxide-semiconductor (CMOS) field-effect transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire (I&F) behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we show that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer., 79 pages, 46 figures, 4 tables

Due to the existence of domain shifts, the distributions of data acquired from different machines show significant discrepancies in industrial applications, which leads to performance degradation of traditional machine learning methods. In this paper, we propose a novel method that combines supervised domain adaptation with prototype learning for fault diagnosis. The proposed method consists of two modules, i.e., feature learning and condition recognition. The module of feature learning applies the Siamese architecture based on one-dimensional convolutional neural networks to learn a domain invariant subspace, which reduces the inter-domain discrepancy of distributions. The module of condition recognition applies a prototypical layer to learn the prototypes of each class. Then the classification task is simplified to find the nearest class prototype. Compared with existing intelligent fault diagnosis methods, this proposed method can be extended to address the problem when the classes from the source and target domains are partially overlapped. The model must generalize to unknown classes in the source domain, given only a few samples of each new target class. The effectiveness of the proposed method is verified using two bearing datasets. The model quickly converges with high classification accuracy using a few labeled target samples in training, even one per class can be effective.

Memristors provide a new paradigm to realize biomimetic and scalable neuron and synapse neuromorphic computing primitives capable of emulating the rich dynamics of biological counterparts without sacrificing the energy consumption and size. Active memristors with volatile resistive switching and negative differential resistance (NDR) can be used to construct electronic neurons. Using VO 2 active memristors, we show that memristor neurons possess most of the known biological neuronal dynamics and all three classes of neuron excitability. The simulated size and power scaling of memristor neurons project toward biologically competitive neuron density and energy efficiency.

This study focuses on developing an optimal setting method for the first integrated coking flue gas desulphurization and denitration device in China. Maintaining the denitration process in a state of optimal economic efficiency has become an issue in production optimization control. This paper proposes a data-based two-stage nonparametric optimization method to optimize the operation of the denitration process. A principal component regression (PCR)-based multiple case fusion case-based reasoning (CBR) method is proposed to obtain the initial optimization set points. To overcome the steady-state modelling difficulties associated with the process, a local modelling method for the coking flue gas denitration process is developed using an improved just-in-time learning (JITL) algorithm. Taking the preset values obtained above as the initial value of an active set algorithm, the optimization problem can be solved in a timely and precise manner. The intelligent setting software has been developed for running industrial applications, and the results demonstrate the effectiveness of the proposed optimization approach.

Robust and globally referenced positioning is the basis of fully autonomous driving in diverse scenarios. Globally navigation satellite system (GNSS) is currently the main source providing globally referenced positioning. Satisfactory accuracy (5~10 meters) can be obtained in sparse areas. However, the GNSS positioning error can go up to even 100 meters in dense urban areas due to the multipath effects and none-line-of-sight (NLOS) reception caused by reflection and blockage from buildings. NLOS is currently the dominant component degrading the performance of GNSS positioning. Recently, the camera is employed to detect the NLOS satellites and then the NLOS are excluded from GNSS calculation. This exclusion can cause severe distortion of satellite distribution, due to the excessive NLOS receptions in deep urban canyon. This paper proposes to correct the NLOS receptions with the aid of 3D LiDAR ranging after detecting NLOS receptions using camera. The sky-pointing monocular camera is firstly employed to detect satellite visibility. Then, the NLOS delay is modeled using the satellite elevation angle, azimuth angle and distance from receiver to possible reflector which is ranged by 3D LiDAR. The pseudorange measurements are corrected using the estimated NLOS delay. Finally, the GNSS positioning is improved using corrected pseudorange measurements and healthy (visible) satellites. The proposed method is verified through real road tests in a deep urbanized canyon of Hong Kong. The result shows that the proposed method can effectively improve the GNSS positioning performance by decreasing the mean error from 22.01 to 14.96 meters.

Accurate identification of critical nodes and regions in a power grid is a precondition and guarantee for safety assessment and situational awareness. Existing methods have achieved effective static identification based on the inherent topological and electrical characteristics of the grid. However, they ignore the variations of these critical nodes and regions over time and are not appropriate for online monitoring. To solve this problem, a novel data-driven dynamic identification scheme is proposed in this paper. Three temporal and three spatial attributes are extracted from their corresponding voltage phasor sequences and integrated via Gini-coefficient and Spearman correlation coefficient to form node importance and relevance assessment indices. Critical nodes and regions can be identified dynamically through importance ranking and clustering on the basis of these two indices. The validity and applicability of the proposed method pass the test on various situations of the IEEE-39 benchmark system, showing that this method can identify the critical nodes and regions, locate the potential disturbance source accurately, and depict the variation of node/region criticality dynamically.

The lithium-ion battery group contains dozens of similar batteries, the inconsistency of characteristics of individual batteries can seriously affect the performance and life of the whole battery pack. In this paper, we propose a dynamic mixed Gaussian model for battery groping. Firstly, the static termination voltage and the capacity are used for clustering according to conventional method. Then clustering results utilize the whole battery discharge sequence and proposed the method for battery grouping. Finally, through actual production standards, the model structure is dynamically adjusted to meet the demand for the battery grouping number. Compared with the actual production, the proposed method proves the effectiveness of the method.

In the production process of lithium-ion battery, the inconsistency of single cell characteristics in battery pack will seriously affect the whole life and performance of battery pack. In this paper, a lithium-ion battery grouping model is proposed, which includes a data preprocessing model and a novel battery grouping method of K-means algorithm to meet the specific requirements. In the battery data preprocessing model, the faulty battery is removed by designing the process data preprocessing method and actual production standards. In the battery matching algorithm, an improved K-means algorithm is proposed. After the battery grouping is completed, the number of batteries in each cluster is equal. Additionally, experiments for battery grouping and method validation are designed. Compared with the actual method, the proposed method proves its effectiveness.

Monitoring the influence of fault towards the product quality is of great importance to modern manufacturing enterprise. Traditional projection to latent structures (PLS) method as well as its variants still face many problems. In this paper, a new improved weighted PLS (IWPLS) is proposed to utilize the local information of the process data, handle noises and build regression models with better generalization capability. The objective function of IWPLS is weighted through calculating the similarity between the target inner matrix (IM) and the other inner matrices (IMs). Two types of weight matrices are given for different process data set. The IWPLS-based monitoring scheme is developed with additional restrains and decomposition operation. A designed numerical experiments and Tennessee Eastman Process (TEP) are employed to evaluate the validity of the proposed method.

As one of the most common and effective quality-relevant fault monitoring techniques, projection to latent structures(PLS) and its improved algorithms have been wildly used in many industries to provide assurance for high-quality products. In this paper, a new enhanced sparse projection to latent structures(ESPLS) algorithm is proposed to achieve quality-relevant fault monitoring with better sensitivity. The algorithm implements sparse orthogonal decomposition on input process variable space. Two indices based on quality-relevant subspace and quality-irrelevant subspace with major variation are developed for fault detection and analysis. Experiments on Tennessee Eastman Process (TEP) chemical benchmark reveal its outstanding performance in fault detection and superior accuracy in differentiating the quality-relevant and irrelevant impact of the given fault.

Data-based explanatory fault diagnosis methods are of great practical significance to modern industrial systems due to their clear elaborations of the cause and effect relationship. Based on Boolean logic, logical analysis of data (LAD) can discover discriminative if-then rules and use them to diagnose faults. However, traditional LAD algorithm has a defect of time-consuming computation and extracts only the least number of rules, which is not applicable for high-dimensional large data set and for fault that has more than one independent causes. In this paper, a novel fast LAD with multiple rules discovery ability is proposed. The fast data binarization step reduces the dimensionality of the input Boolean vector and the multiple independent rules are searched using modified mixed integer linear programming (MILP). A Case Study on Tennessee Eastman Process (TEP) reveals the superior performance of the proposed method in reducing computation time, extracting more rules and improving classification accuracy.