Sensor-based Method Research Articles

Human Activity Classification Based on Angle Variance Analysis Utilizing the Poincare Plot

We propose a single sensor-based activity classification method where the Poincare plot was introduced to analyze the variance of the angle between acceleration vector with gravity calculated from the raw accelerometer data for human activity classification. Two datasets named ‘Human Activity Recognition’ and ‘MHealth dataset’ were used to develop the model to classify activity from low to vigorous intensity activities and posture estimation. Short-term and long-term variability analyzing the property of the Poincare plot was used to classify activities according to the vibrational intensity of body movement. Commercially available Actigraph’s activity classification metric ‘count’ resembled value was used to compare the feasibility of the proposed classification algorithm. In the case of the HAR dataset, laying, sitting, standing, and walking activities were classified. Poincare plot parameters SD1, SD2, and SDRR of angle in the case of angle variance analysis and the mean count of X-, Y-, and Z-axis were fitted to a support vector machine (SVM) classifier individually and jointly. The variance- and count-based methods have 100% accuracy in the static–dynamic classification. Laying activity classification has 100% accuracy from other static conditions in the proposed method, whereas the count-based method has 98.08% accuracy with 10-fold cross-validation. In the sitting–standing classification, the proposed angle-based algorithm shows 88% accuracy, whereas the count-based approach has 58% accuracy with a support vector machine classifier with 10-fold cross-validation. In the classification of the variants of dynamic activities with the MHealth dataset, the accuracy for angle variance-based and count-based methods is 100%, in both cases, for fivefold cross validation with SVM classifiers.

A Vibration Sensor-Based Method for Generating the Precise Rotor Orbit Shape with General Notch Filter Method for New Rotor Seal Design Testing and Diagnostics.

Verification of the behaviour of new designs of rotor seals is a crucial phase necessary for their use in rotary machines. Therefore, experimental equipment for the verification of properties that have an effect on rotor dynamics is being developed in the test laboratories of the manufacturers of these components all over the world. In order to be able to compare the analytically derived and experimentally identified values of the seal parameters, specific requirements for the rotor vibration pattern during experiments are usually set. The rotor vibration signal must contain the specified dominant components, while the others, usually caused by unbalance, must be attenuated. Technological advances have made it possible to use magnetic bearings in test equipment to support the rotor and as a rotor vibration exciter. Active magnetic bearings allow control of the vibrations of the rotor and generate the desired shape of the rotor orbit. This article presents a solution developed for a real test rig equipped with active magnetic bearings and rotor vibration sensors, which is to be used for testing a new design of rotor seals. Generating the exact shape of the orbit is challenging. The exact shape of the rotor orbit is necessary to compare the experimentally and numerically identified properties of the seal. The generalized notch filter method is used to compensate for the undesired harmonic vibrations. In addition, a novel modified generalized notch filter is introduced, which is used for harmonic vibration generation. The excitation of harmonic vibration of the rotor in an AMB system is generally done by injecting the harmonic current into the control loop of each AMB axis. The motion of the rotor in the AMB axis is coupled, therefore adjustment of the amplitudes and phases of the injected signals may be tedious. The novel general notch filter algorithm achieves the desired harmonic vibration of the rotor automatically. At first, the general notch filter algorithm is simulated and the functionality is confirmed. Finally, an experimental test device with an active magnetic bearing is used for verification of the algorithm. The measured data are presented to demonstrate that this approach can be used for precise rotor orbit shape generation by active magnetic bearings.

Satisfying Task Completion and Assist-as-Needed Performance in Robotic Exoskeletons

Assist-as-needed (AAN) control approaches aim to minimize the intervention of robots in robot-aided rehabilitation therapy when patients are able to perform the task. The existing approaches generally contain a forgiving controller which fails to satisfactorily complete the task when patients stay passive or apply resistive/opposing forces. Task completion in such situations is important since it efficiently trains and guides users on how to perform the exercise during active participation. In this study, we have developed an AAN control scheme that provides significant freedom to users when they can perform the exercise while it provides sufficient support for the precise completion of the task when users are not able to do so. These features have been achieved by modulating the impedance of the robot and designing the controller in the velocity domain. A new velocity tracking controller is proposed which does not utilize velocity measurements, and two methods are considered to measure human inputs: force sensor-based method and disturbance observer-based method. In the first approach, the controller does not require the dynamics of the human-robot system, and in the second approach, partial information of the dynamics is required. The performance of the controller is evaluated on an upper-limb robotic exoskeleton. Note to Practitioners-The classic control methods utilized in rehabilitation robots for the active participation of patients are usually low-gain controllers that allow patients to deviate from the desired motion path. However, this approach results in poor task completion when users are not able to do the task. This paper proposes a control scheme for robotic exoskeletons that simultaneously satisfies the two objectives, namely, the low intervention of the robot when users can perform the task, and precise task completion when users cannot handle it. The proposed controller does not require velocity measurements and the precise dynamic model of the system. The approach needs human intention detection, which can be achieved by using force sensors or a disturbance observer. While the second method reduces the complexity and cost of the mechanical parts, it requires the dynamic model of the robot. The tests conducted on an upper-limb robotic exoskeleton show the satisfactory performance of both methods.

Neural Nonlinear Autoregressive Model with Exogenous Input (NARX) for Turboshaft Aeroengine Fuel Control Unit Model

One of the most important parts of a turboshaft engine, which has a direct impact on the performance of the engine and, as a result, on the performance of the propulsion system, is the engine fuel control system. The traditional engine control system is a sensor-based control method, which uses measurable parameters to control engine performance. In this context, engine component degradation leads to a change in the relationship between the measurable parameters and the engine performance parameters, and thus an increase of control errors. In this work, a nonlinear model predictive control method for turboshaft direct fuel control is implemented to improve engine response ability also in presence of degraded conditions. The control objective of the proposed model is the prediction of the specific fuel consumption directly instead of the measurable parameters. In this way is possible decentralize controller functions and realize an intelligent engine with the development of a distributed control system. Artificial Neural Networks (ANN) are widely used as data-driven models for modelling of complex systems such as aeroengine performance. In this paper, two Nonlinear Autoregressive Neural Networks have been trained to predict the specific fuel consumption for several transient flight maneuvers. The data used for the ANN predictions have been estimated through the Gas Turbine Simulation Program. In particular the first ANN predicts the state variables based on flight conditions and the second one predicts the performance parameter based on the previous predicted variables. The results show a good approximation of the studied variables also in degraded conditions.

Port Structure Inspection Based on 6-DOF Displacement Estimation Combined with Homography Formulation and Genetic Algorithm

A vision sensor-based 6-DOF displacement evaluation method incorporating a genetic algorithm was proposed to monitor the critical defects of port infrastructure, such as deflection, slope, and slip. The 6-DOF behavior of the port structure, including subsidence, was estimated based on the specification of the target and fixed structures nearby. The method calculates the relative position of the target port structure and measures the movement of the structure over time. To improve the measurement accuracy, a genetic algorithm was used to adjust the intrinsic parameters that were previously estimated using the checkerboards. The results of measuring 6-DOF displacements based on the tuned intrinsic parameters confirmed that it has the potential to accurately measure the 6-DOF behavior of port facilities. The possibility of field application was examined through an artificial movement that was induced in the image of the port facility to create an arbitrary displacement between two points.

Wearable motion sensor-based chewing side detection

Chewing side preference means a tendency to use one side to chew food more frequently than the other. Medical studies show that chewing side preference can result in lateral facial asymmetry, teeth abrasion, temporomandibular disorders, malocclusion, and stomach illness. To continuously detect chewing side preference and quantify its severity in daily life, several wearable sensor-based methods have been proposed in recent years. However, these methods are either intrusive or not fine-grained enough. In this paper, we propose a wearable motion sensor-based chewing side detection method. We observe that chewing activity generates mastication muscle bulge and skull vibration, which can be sensed by motion sensors worn on the mastication muscles. In addition, the muscle bulge and skull vibration of the chewing side are different from those of the non-chewing side. These observations motivate us to deploy motion sensors on the left and right temporalis muscles to detect chewing sides. We propose a heuristic-rules based method to exclude non-chewing data and segment each chew accurately. The relative difference series of the left and right sensors are then calculated to characterize the difference of muscle bulge and skull vibration between the chewing side and the non-chewing side. A two-class classifier is trained using long short-term memory (LSTM), an artificial recurrent neural network, to model the data samples and classify chewing sides. A real-world evaluation dataset of eight food types is collected from eight human subjects. The average detection accuracy reaches 84.8%. The highest detection accuracy for a single subject is up to 97.4%.

Establishing Reference Values for Temporal Kinematic Swallow Events Across the Lifespan in Healthy Community Dwelling Adults Using High-Resolution Cervical Auscultation.

Few research studies have investigated temporal kinematic swallow events in healthy adults to establish normative reference values. Determining cutoffs for normal and disordered swallowing is vital for differentially diagnosing presbyphagia, variants of normal swallowing, and dysphagia; and for ensuring that different swallowing research laboratories produce consistent results in common measurements from different samples within the same population. High-resolution cervical auscultation (HRCA), a sensor-based dysphagia screening method, has accurately annotated temporal kinematic swallow events in patients with dysphagia, but hasn't been used to annotate temporal kinematic swallow events in healthy adults to establish dysphagia screening cutoffs. This study aimed to determine: (1) Reference values for temporal kinematic swallow events, (2) Whether HRCA can annotate temporal kinematic swallow events in healthy adults. We hypothesized (1) Our reference values would align with a prior study; (2) HRCA would detect temporal kinematic swallow events as accurately as human judges. Trained judges completed temporal kinematic measurements on 659 swallows (N = 70 adults). Swallow reaction time and LVC duration weren't different (p > 0.05) from a previously published historical cohort (114 swallows, N = 38 adults), while other temporal kinematic measurements were different (p < 0.05), suggesting a need for further standardization to feasibly pool data analyses across laboratories. HRCA signal features were used as input to machine learning algorithms and annotated UES opening (69.96% accuracy), UES closure (64.52% accuracy), LVC (52.56% accuracy), and LV re-opening (69.97% accuracy);providing preliminary evidence that HRCA can noninvasively and accurately annotate temporal kinematic measurements in healthy adults to determine dysphagia screening cutoffs.

A LiDAR Sensor-Based Spray Boom Height Detection Method and the Corresponding Experimental Validation.

Sprayer boom height (Hb) variations affect the deposition and distribution of droplets. An Hb control system is used to adjust Hb to maintain an optimum distance between the boom and the crop canopy, and an Hb detection sensor is a key component of the Hb control system. This study presents a new, low-cost light detection and ranging (LiDAR) sensor for Hb detection developed based on the principle of single-point ranging. To examine the detection performance of the LiDAR sensor, a step height detection experiment, a field ground detection experiment, and a wheat stubble (WS) height detection experiment as well as a comparison with an ultrasonic sensor were performed. The results showed that the LiDAR sensor could be used to detect Hb. When used to detect the WS height (HWS), the LiDAR sensor primarily detected the WS roots and the inside of the WS canopy. HWS and movement speed of the LiDAR sensor (VLiDAR) has a greater impact on the detection performance of the LiDAR sensor for the WS canopy than that for the WS roots. The detection error of the LiDAR sensor for the WS roots is less than 5.00%, and the detection error of the LiDAR sensor for the WS canopy is greater than 8.00%. The detection value from the LiDAR sensor to the WS root multiplied by 1.05 can be used as a reference basis for adjusting Hb, and after the WS canopy height is added to the basis, the value can be used as an index for adjusting Hb in WS field spraying. The results of this study will promote research on the boom height detection method and autonomous Hb control system.

An FBG Strain Sensor-Based NPW Method for Natural Gas Pipeline Leakage Detection

Natural gas pipeline leaks can lead to serious and dangerous accidents that can cause great losses of life and property. Therefore, detecting natural gas pipeline leaks has always been an important subject. The negative pressure wave (NPW) method is currently the most widely used leakage detection method. Generally, this method uses pressure sensors to detect NPW signals to assess the leak and determine the location of the leakage point. However, the installation of a pressure sensor requires penetrating the pipeline structure, so the sensor intervals are often distant, leading to large signal attenuations and the ineffective detection of small leaks. An NPW method based on fiber Bragg grating (FBG) strain sensors is proposed in this paper which detects NPWs by monitoring the annular strain of the pipeline. Moreover, due to the advantages of nondestructive installation FBG strain sensors can be arranged closer along the distance of the pipeline, the attenuation of the NPW is small and the detection of leaks is improved. This method is tested through experiments and compared with a pressure sensor-based method; the experimental results verify that the proposed method is more effective in detecting natural gas pipeline leaks.

Field-deployable measurement technique for absolute acoustic nonlinearity parameter values

Over the past two decades, several researchers have demonstrated that changes in the acoustic nonlinearity parameter β can be related to changes in material properties due to mechanical and/or thermal degradation processes. Generally, a piezoelectric sensor-based detection method is used to measure changes in β values because it is operationally simpler than the complex capacitive detection method. However, this method is limited to measuring only relative changes in β values; whereas, the absolute β values of components in service often need to be measured in the field to quantify the degradation level. Accordingly, a novel field-deployable method for measuring absolute β values was developed in this study. Nonlinear ultrasonic experiments were conducted using capacitive detection, conventional piezoelectric sensor-based detection, and proposed detection methods, and the results were compared. The β values of a copper single-crystal sample measured using the new and the capacitive detection methods were 2.49 and 2.1, respectively, and those obtained using the conventional piezoelectric sensor-based detection method ranged between 90 and 130. The test results confirm that the proposed field-deployable measurement method produces more consistent absolute β values without involving the complexity of the capacitive detection method.

Novel sensor-based method (quick test) for the in-season rapid evaluation of herbicide efficacy under real field conditions in durum wheat

AbstractOptimum herbicide use is a key factor affecting the success of any integrated weed management strategy. The main objective of the current study was to implement a method based on spectrometer measurements for the in situ evaluation of herbicide efficacy and the detection of potentially herbicide-resistant weeds. Field trials were conducted in Greece between 2018 and 2020 in several durum wheat fields (Triticum durum Desf.). In all trials, the overall effect of herbicide application on the recorded Normalized Difference Vegetation Index (NDVI) values (at 1 and 2 wk after treatment [WAT]) was significant (P ≤ 0.001). For the majority of the surveyed fields, low NDVI values were recorded after 2,4-D application and a mixture of clopyralid + florasulam from 1 WAT, suggesting their increased efficacy. In several cases, the application of pyroxsulam + florasulam resulted in significant NDVI reductions at 2 WAT. As observed at the end of the growing seasons, the herbicides that reduced NDVI resulted in lower weed biomass. Strong correlations were observed between weed aboveground biomass and NDVI (2 WAT). In particular, R2 values were 0.8234 to 0.8649, 0.8453, 0.8595, 0.8149, and 0.8925 for the Aliartos, Thiva, Domokos, Larissa, and Orestiada fields, respectively. The overall effects of herbicide application on wheat grain yield were also significant (P ≤ 0.001). Pot experiments confirmed that the high NDVI values in some cases could be attributed to the presence of herbicide-resistant weeds. For instance, the resistance indices of two accessions of catchweed bedstraw (Galium aparine L.) to mesosulfuron-methyl + iodosulfuron-methyl-sodium ranged between 9.7 and 13.2, whereas one sterile oat [Avena sterilis L. ssp. ludoviciana (Durieu) Gillet &amp; Magne] accession was 8.8 times more resistant to fenoxaprop-p-ethyl than a susceptible one. The present study is targeted at making a significant contribution toward establishing cause–effect relationships and presenting a useful tool for developing more effective weed management practices in more arable crops and under different soil and climatic conditions.

IoE based framework for smart agriculture

The Internet of Everything (IoE) is an advanced platform which provides a common network among people, process, data, and things. It provides a strong turning tool which converts the information into actions. The growing penetration of Internet of Things (IoT) into every aspect of our life brings a logical sense regarding the adoption of IoE for the efficient agricultural process. The objective of this work is two-fold. First, it proposes an efficient IoE based smart agricultural model (IoESA) as a new AgriTech tool for efficient communication among all IoT based devices. Secondly,it proposes a sensor-based soil quality monitoring method called Soil-Smart Agriculture (SSA) that helps the farmers to get the current soil quality. The result section of the paper consists of two outcomes. The first one is an strong interconnected IoE framework for smart agriculture. Secondly, an case study called SSA, which accumulated soil data and transfers that data through IoESA-MIS at the time of requirement.

Sensorless parameter estimation of VFD based cascade centrifugal pumping system using automatic pump curve adaption method

The Sustainable Development Goal 7 necessitates efficient energy utilization and aligning energy efficiency targets to industrial machines would pave a way for prospective developments. Pumps are inevitable in commercial and industrial sectors and they contribute to nearly 22% of the global industrial energy utilization. Attesting the fact, this paper focuses on the energy-efficient operation of a pumping system by governing the speed using Variable Frequency Drives (VFDs). This is accomplished by modeling the pump performance curves by estimating the parameters like flow rate and pressure values for automatic adaptation in VFDs. With the aid of the manufacturers’ datasheet and the operating state of the pump, the pump performance curve is estimated automatically. The pump operating point is attained from pump parameters and motor rotational speed without using the sensors (sensor-less). This proposed methodology is validated through an experimental setup with a 1.1 kW Induction motor (IM) based centrifugal pump driven by 2.2 kW Danfoss VFD and the obtained results can be adopted for estimating the operating point of the centrifugal pumps throughout its operating range. Further, the variations in accuracy between actual and measured flow rate for the proposed scheme is found to be ranging from −5.29 GPM to 2.03 GPM which is about −0.243% to 0.0713%. Specifically, the pump flow rate estimation accuracy deviation is found to be between 2%–8% during lower operating flow rates and is within 2% during the higher operating region. Further, the proposed scheme offers better efficiency than the conventional sensor-based method which constitutes about 2% deviation in accuracy. Consequently, it empowers Industrial users to perform energy-efficient optimization for the cascade pumping system.

A Low-Cost Tire Pressure Loss Detection Framework Using Machine Learning

The tire pressure state directly affects the safety and economy of the vehicle. Its monitoring is gradually becoming a necessary function for all types of vehicles. However, the existing sensor-based monitoring method is costly and without any redundancy when the sensor fails, which hinders its wide applications. This article proposed a machine learning-based framework without any additional sensors. First, the rigid tire model is introduced for feature extraction. Then, the manufacturing error in speed gear is calculated and removed by the recursive least square method. Next, the features in time- and frequency-domain of speed signals are extracted. Finally, based on the suitable signals filtered by the decision tree, the normal and pressure-loss judgment are given out by support vector machine and final synthesis. The results show that the proposed framework has 96.18% report accuracy and has the potential for further estimation of the characteristics in tires and roads.

An Algorithmic Approach for Quantitative Evaluation of Parkinson’s Disease Symptoms and Medical Treatment Utilizing Wearables and Multi-Criteria Symptoms Assessment

The paper presents a novel sensor-based disease symptoms evaluation method which can be applied in the domain of neurological treatment monitoring and efficiency analysis. The main purpose of the method is to provide a quantitative approach for symptoms recognition and their intensity, which can be used for efficient medicine intake planning for Parkinson's Disease patients. This work presents an innovative method, which enables to objectify the process of clinical trials. The developed solution implements sensor data fusion method, which analyses time correlated wearable sensor biomedical data and symptoms survey. We have merged two separate methods of recognizing and assessing the intensity of Parkinson's Disease (PD) symptoms using time-constrained survey as well as sensor and interaction-based algorithms, which enable to objectively assess the intensity of disease symptoms. Based on process-based analysis and clinical trials observations, a set of requirements for validating symptoms of neurological diseases have been formulated. Proposed solution concentrates on PD indicators connected with arms movement and mental reaction delays, which can be registered using wearable sensors. Since 2017 the tool has been tested by a group of four selected neurologists and 10 users, 3 of which are PD patients. To meet the project's supplementary (efficiency, security) requirements, a test clinical trial has been performed involving 3 patients executing trials which lasted two weeks and was supported by the continuous application usage. After successful deployment the method and software tools has been presented for commercial use and further development in order to adjust its usage for other neurological disorders.

Deep Learning-Based PDR Scheme That Fuses Smartphone Sensors and GPS Location Changes

Pedestrian dead reckoning (PDR), a sensor-based localization method using a smartphone, combines multi-sensor data from an inertial measurement unit (IMU) generated by the movement of pedestrians and calculates the amount of movement change from a previous location using fusion of sensor data. In this study, we propose a method to improve the efficiency of a deep learning (DL)-based PDR scheme to solve problems associated with the existing PDR method. The proposed DL-PDR scheme solves the movement change of smartphone users as a regression problem by combining IMU and global positioning system (GPS) data. In this paper, we (1) describe the existing PDR methods and problems, describe the proposed DL-PDR scheme and the data collection process of the input sensor data and output GPS used for deep learning, (2) correlate the collected I/O data and conduct preprocessing to make the data suitable for learning, (3) apply data refining and data augmentation methods to provide efficient learning and prevent overfitting, and (4) Verify the performance of the proposed scheme. The localization performance between the proposed scheme and existing methods is compared in various buildings where continuous localization is possible owing to connected indoor/outdoor spaces.

A Novel Sensor-Based Human Activity Recognition Method Based on Hybrid Feature Selection and Combinational Optimization

In recent years, sensor-based human activity recognition (HAR) has become a hot topic due to the advancement of sensing technologies, wireless communication technologies and nano-technologies. Since the sensor signals are usually non-stationary and quite noisy, both selecting the discriminant feature representations and finding out the optimal parameters for recognition algorithm play an important role for the enhanced performance and robustness of an HAR system. However, most of the previous research focused on one of them ignoring their interactions. Very few studies focused on these two aspects simultaneously. Considering the two factors separately may lead to inferior HAR performance. This paper presents a novel HAR framework which can optimize the feature set and the parameters of recognition algorithm synchronously for robust and optimal system performance. A new hybrid feature selection methodology using game-theory based feature selection (GTFS) and binary firefly algorithm (BFA), called GTFS-BFA, is proposed. GTFS-BFA is a hybrid methodology combining evidence from both filter and wrapper feature selection methods. It consists of two phases, namely pre-selection phase and re-selection phase. Pre-selection phase relies on game-theory-based filter method, while the re-selection phase uses binary firefly algorithm (BFA) as a wrapper method. The popular and efficient algorithm kernel extreme learning machine (KELM) is utilized as a classifier. The experimental results indicate that the proposed method can obtain better comprehensive performance in terms of four performance measures through a comparison to other existing methods on daily activity dataset from five body positions.

Research on the estimation method of the point-of-interest (POI) displacement for ultra-precision flexible motion system based on functional optical fiber sensor

This paper proposes a POI displacement estimation method based on the functional optical fiber sensor and the phase modulation principle to improve the POI displacement estimation accuracy. First, the relation between the object deformation and the optic fiber lightwave phase is explained; the measurement principle of functional optical fiber sensor based on the heterodyne interference principle and its layout optimization method is proposed, and a POI displacement estimation model is presented based on the data approach. Secondly, a beam is taken as the simulation object, the optimal position and length of the optical fiber sensor are determined based on its simulation data. Finally, the experimental device is designed to verify the effectiveness of the POI displacement estimation method based on the optic fiber sensors. The frequency-domain plot of the signals shows that the optical fiber sensors can express the flexible deformation of the analyzed object well. The POI displacement estimation model with the fiber optic sensor signals as one of the inputs is constructed. Through estimating the test data, the error using the optical fiber sensor-based POI displacement estimation method proposed in this paper reduces by more than 61% compared to the rigid body-based assumption estimation method.

Optimization of Intelligent Logistics Supply Chain Management System Based on Wireless Sensor Network and RFID Technology

Based on the in‐depth study of two technologies, wireless sensor network, and RFID technology, this paper proposes a corresponding solution to the urgent storage positioning problem and the safety of goods in transit in the logistics supply chain process. And on this basis, it broadens the whole logistics process of warehousing and distribution and designs a framework of intelligent logistics supply chain management system with wireless sensor network and RFID technology to realize the management process of logistics main business. For the problems of complicated hardware wiring, the excessive number of positioning nodes and low effective coverage rate of traditional RFID‐based positioning methods in large‐scale warehousing environment, the integrated RFID and wireless sensor network fusion technology constructs a new movable anchor node, divides the nodes in the traditional positioning process into fixed anchors and mobile anchors, proposes a secondary positioning method, improves positioning accuracy and effective coverage rate, and provides a new positioning method for large‐scale storage environment. For the transportation of goods in transit, especially precious and dangerous goods, a six‐axis acceleration sensor‐based method for detecting the behavioral attitude of goods in transit is proposed. Time window slicing of cargo motion data stream is performed to reduce data processing and based on the statistical features of a large amount of experimental data, and feature vectors and classifiers are analyzed and designed to achieve behavioral attitude classification of goods in transit. The experiment proves that this method has high recognition accuracy and has certain practical application value. Finally, the whole storage and distribution logistics process is integrated, the framework of intelligent logistics supply chain management system combining WSN and RFID is built based on wireless sensor network software and hardware platform, and the detailed design of logistics supply chain management system business process is mainly carried out. Combined with the two key methods proposed in this paper, the information management process of traditional logistics business such as access, storage positioning and monitoring, and distribution monitoring and management is realized, and the management system is tested on this basis. The testing effect proves that this system has certain deployment reference significance and practical application value and to a certain extent can help improve the intelligence level of logistics supply chain management and achieve the expected goal of this study.

Minimum Distance-Based Detection of Incipient Induction Motor Faults Using Rayleigh Quotient Spectrum of Conditioned Vibration Signal

In this article, we propose a single-vibration sensor-based method for detecting incipient faults in squirrel cage induction motors (SCIMs). We consider defects in different parts of the bearing (inner raceway, outer raceway, cage train, and rolling element) and in a single bar of the rotor. The vibration signal is dominated by the fundamental rotational frequency and its harmonics. The dominant components result in numerical errors while estimating the relatively indistinct fault-specific spectral components. In this article, we precondition the vibration signal by suppressing multiple dominant components using an extended Kalman filter-based method. The suppression of the dominant components reduces the spectral leakage, exposes minute fault components, and improves the overall amplitude estimation. Subsequently, we estimate the fault frequency and amplitude using an accurate and low-complexity Rayleigh-quotient-based spectral estimator. The thresholds for fault detection are determined from a small number of healthy data, and an adaptive minimum distance-based detector is used for hypothesis testing. The proposed test improves detection and reduces false alarms under noisy conditions. We test the complete algorithm using data from a 22-kW SCIM laboratory setup. The proposed method has achieved 100% accuracy with the publicly available 12-kHz drive-end bearing data from Case Western Reserve University, Cleveland, OH, USA.