In the modern processed world, it becomes more necessary to certify humans in a very secure way. There are modern square measure applications such as online banking or online search usage techniques that are depended on passwords, keys or individual identification card. These technologies and processes carry the danger that information may be forgotten, lost, or perhaps stolen. Therefore, the forms of identification promise a singular thanks to being ready to certify humans. A secure and confidential identification technique is the use of fingerprints. We proposed here IoT and Blockchain Based Intelligence Security System for Human Detection using an Improved ACO and Heap Algorithm. We proposed IoT and Blockchain Based Technology for ensuring the security of our system.

IoT and Blockchain Based Intelligence Security System for Human Detection using an Improved ACO and Heap Algorithm

For the sake of solving the problem of the difficulty of extracting fault features under the background of noise and accurately identify the state of the bearing, a compound fault diagnosis method of rolling bearing based on parameter optimization maximum correlated kurtosis deconvolution (MCKD) and convolutional neural network (CNN) is proposed. First, the adaptive multi-strategy cuckoo search algorithm (MSACS) is used to iteratively optimize the important parameters of MCKD. Second, the optimized MCKD is used to filter and denoise the rolling bearing fault signal, and the denoised signal is obtained. Finally, the denoised signal is input to the CNN model for training and testing to obtain the classification result of fault diagnosis. Through the test and evaluation of the fault dataset, the proposed method is compared with particle swarm optimization (PSO) parameter optimization method (PSO-MCKD-CNN) and CNN method without noise reduction. At the same time, it is compared with other advanced methods. The experimental results shows that this method improves the diagnostic performance of the neural network, obtains higher diagnostic accuracy, and is more conducive to the detection of compound faults.

Rolling Bearing Compound Fault Diagnosis Based on Parameter Optimization MCKD and Convolutional Neural Network

Stochastic hybrid system approach to task-orientated remaining useful life prediction under time-varying operating conditions

The rotating performance of motorized spindles is critical to the dimensional accuracy and surface quality of the parts to be machined. This paper proposes a novel measurement strategy and an evaluation method of spindle's rotating performance, considering the loads imposed on the spindle. To be specific, cutting loads are simplified as a unified function to guide the load simulation, and the load-induced error is eliminated using a position adjustment procedure. In a certain speed-load combination, the spindle's rotating performance under the unified load function is measured using a data envelope method. To extend the assessment to all typical work conditions, an evaluation scheme for different speed and load combinations is proposed based on a visual ideal of Multi-Zones Error Map (MZE Map). The necessity of considering the load-dependent rotating performance is demonstrated via several tests conducted on proposed load simulation platform. Besides, the procedures for developing the MZE Map are performed, and the results express an overview relationship between rotating performance and different speed-load conditions. The proposed method provides a new approach to evaluate the rotating performance considering the actual load conditions. This load-dependent oriented evaluating strategy contributes to a more precise health evaluation and management method for motorized spindles in actual machining process.

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Load-Dependent Rotating Performance of Motorized Spindles: Measurement and Evaluation Using Multi-Zones Error Map

Improved sliding mode dynamic matrix control strategy: Application on spindle loading and precision measuring device based on piezoelectric actuator

As the key functional component of the machine tool, the reliability test of the spindle is necessary to verify the reliability of the machine tool. In the reliability test, the cutting load model is the guideline of workload simulation and is the prerequisite to ensure the accuracy and effectiveness of the long-term experiment. However, the existing load models usually aim at the specific cutting force at the tool-tip, thereby ignoring the versatility, maneuverability, and accuracy of the load model when applied in the spindle reliability test. In this study, a general cutting load model for the machine tool spindle is established in a form of radial-axial-torque decomposition, and the radial force is simplified as non-rotating status for the maneuverability of conducting a load simulation. The difference between rotating and non-rotating radial force on the reliability calculation is also discussed and corrected using bearing fatigue analysis. A spindle reliability test platform with radial force, axial force, and torque simulation is developed according to the cutting load model, while the loading spectrum is compiled for conducting the spindle reliability test. This research is of great engineering value for the designing of the spindle reliability test.

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Weizheng Chen

Papers

Stochastic hybrid system approach to task-orientated remaining useful life prediction under time-varying operating conditions

Load-Dependent Rotating Performance of Motorized Spindles: Measurement and Evaluation Using Multi-Zones Error Map

Improved sliding mode dynamic matrix control strategy: Application on spindle loading and precision measuring device based on piezoelectric actuator

General Cutting Load Model for Workload Simulation in Spindle Reliability Test

Improved sliding mode dynamic matrix control strategy: Application on spindle loading and precision measuring device based on piezoelectric actuator