scholarly journals AI-Enabled Predictive Maintenance Framework for Autonomous Mobile Cleaning Robots

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 13
Author(s):  
Sathian Pookkuttath ◽  
Mohan Rajesh Elara ◽  
Vinu Sivanantham ◽  
Balakrishnan Ramalingam
Keyword(s):  
Early Stage ◽  
Vibration Signal ◽  
Field Trials ◽  
Performance Degradation ◽  
Predictive Maintenance ◽  
Vibration Signals ◽  
Safety Issues ◽  
Cleaning Robot ◽  
Operational Safety ◽  
Cleaning Robots

Vibration is an indicator of performance degradation or operational safety issues of mobile cleaning robots. Therefore, predicting the source of vibration at an early stage will help to avoid functional losses and hazardous operational environments. This work presents an artificial intelligence (AI)-enabled predictive maintenance framework for mobile cleaning robots to identify performance degradation and operational safety issues through vibration signals. A four-layer 1D CNN framework was developed and trained with a vibration signals dataset generated from the in-house developed autonomous steam mopping robot ‘Snail’ with different health conditions and hazardous operational environments. The vibration signals were collected using an IMU sensor and categorized into five classes: normal operational vibration, hazardous terrain induced vibration, collision-induced vibration, loose assembly induced vibration, and structure imbalanced vibration signals. The performance of the trained predictive maintenance framework was evaluated with various real-time field trials with statistical measurement metrics. The experiment results indicate that our proposed predictive maintenance framework has accurately predicted the performance degradation and operational safety issues by analyzing the vibration signal patterns raised from the cleaning robot on different test scenarios. Finally, a predictive maintenance map was generated by fusing the vibration signal class on the cartographer SLAM algorithm-generated 2D environment map.

Hydrogen Commonly Applicable to Medicine to Agriculture: From Molecular Mechanisms to the Field

2020 ◽  
Vol 26 ◽  
Author(s):  
Longna Li ◽  
Wang Lou ◽  
Lingshuai Kong ◽  
Wenbiao Shen
Keyword(s):  
Molecular Mechanisms ◽  
Field Trials ◽  
Hydrogen Gas ◽  
Agricultural Products ◽  
Nitrite Accumulation ◽  
Low Carbon ◽  
Plant Tolerance ◽  
Low Carbon Economy ◽  
Biotic Stresses ◽  
Safety Issues

Abstract:: The emerging field of hydrogen biology has to date mainly been applied in medicine. However, hydrogen biology can also enable positive outcomes in agriculture. Agriculture faces significant challenges resulting from a growing population, climate change, natural disasters, environment pollution, and food safety issues. In fact, hydrogen agriculture is a practical application of hydrogen biology, which may assist in addressing many of these challenges. It has been demonstrated that hydrogen gas (H2) may enhance plant tolerance towards abiotic and biotic stresses, regulate plant growth and development, increase nutritional values, prolong the shelf life, and decrease the nitrite accumulation during the storage of vegetables, as well as increase the resilience of livestock to pathogens. Our field trials show that H2 may have a promising potential to increase yield and improve the quality of agricultural products. This review aims to elucidate mechanisms for a novel agricultural application of H2 in China. Future development of hydrogen agriculture is proposed as well. Obviously, hydrogen agriculture belongs to low carbon economy, and has great potential to provide “safe, tasty, healthy, and highyield” agricultural products so that it may improve the sustainability of agriculture.

scholarly journals HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 660 ◽  
Author(s):  
Fang Liu ◽  
Liubin Li ◽  
Yongbin Liu ◽  
Zheng Cao ◽  
Hui Yang ◽  
...  
Keyword(s):  
Kernel Method ◽  
Back Propagation ◽  
Vibration Signal ◽  
Back Propagation Neural Network ◽  
Industrial Applications ◽  
Performance Degradation ◽  
Support Vector ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Learning Machine

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

Rolling bearing degradation condition clustering using multidimensional degradation feature and Gath–Geva fuzzy clustering algorithm

2020 ◽  
pp. 107754632095495
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Tao X Mei ◽  
Sun D Jian ◽  
Wang Wei
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Time Parameter ◽  
Mechanical Equipment ◽  
Time Dimension

In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C0 complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C0 complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C0 complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

scholarly journals Learning to See the Vibration: A Neural Network for Vibration Frequency Prediction

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2530 ◽  
Author(s):  
Jiantao Liu ◽  
Xiaoxiang Yang
Keyword(s):  
Neural Network ◽  
Vibration Frequency ◽  
Frequency Estimation ◽  
Vibration Signal ◽  
Frequency Measurement ◽  
Optical Methods ◽  
Vibration Measurement ◽  
Vibration Signals ◽  
Engineering Practices ◽  
To Receive

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrate that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.

scholarly journals Compressive sensing-based vibration signal reconstruction using sparsity adaptive subspace pursuit

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879087 ◽  
Author(s):  
Lin Zhou ◽  
Qianxiang Yu ◽  
Daozhi Liu ◽  
Ming Li ◽  
Shukai Chi ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Data Storage ◽  
Wireless Sensors ◽  
Signal Reconstruction ◽  
Estimation Method ◽  
Vibration Signal ◽  
Estimation Accuracy ◽  
Offshore Structure ◽  
Vibration Signals ◽  
Reconstructed Signal

Wireless sensors produce large amounts of data in long-term online monitoring following the Shannon–Nyquist theorem, leading to a heavy burden on wireless communications and data storage. To address this problem, compressive sensing which allows wireless sensors to sample at a much lower rate than the Nyquist frequency has been considered. However, the lower rate sacrifices the integrity of the signal. Therefore, reconstruction from low-dimension measurement samples is necessary. Generally, the reconstruction needs the information of signal sparsity in advance, whereas it is usually unknown in practical applications. To address this issue, a sparsity adaptive subspace pursuit compressive sensing algorithm is deployed in this article. In order to balance the computational speed and estimation accuracy, a half-fold sparsity estimation method is proposed. To verify the effectiveness of this algorithm, several simulation tests were performed. First, the feasibility of subspace pursuit algorithm is verified using random sparse signals with five different sparsities. Second, the synthesized vibration signals for four different compression rates are reconstructed. The corresponding reconstruction correlation coefficient and root mean square error are demonstrated. The high correlation and low error result mean that the proposed algorithm can be applied in the vibration signal process. Third, implementation of the proposed approach for a practical vibration signal from an offshore structure is carried out. To reduce the effect of signal noise, the wavelet de-noising technique is used. Considering the randomness of the sampling, many reconstruction tests were carried out. Finally, to validate the reliability of the reconstructed signal, the structure modal parameters are calculated by the Eigensystem realization algorithm, and the result is only slightly different between original and reconstructed signal, which means that the proposed method can successfully save the modal information of vibration signals.

Performance degradation analysis of solid lubricated bearings based on Laplace wavelet filter and self-organizing map

2013 ◽  
Vol 228 (10) ◽  
pp. 1743-1753 ◽  
Author(s):  
Chao Zhang ◽  
Shaoping Wang
Keyword(s):  
Feature Vector ◽  
Vibration Signal ◽  
Degradation Process ◽  
Performance Degradation ◽  
Friction Torque ◽  
Self Organizing Map ◽  
Wavelet Filter ◽  
Degradation Analysis ◽  
Space Mechanisms ◽  
Self Organizing

Solid lubricated bearings are commonly used in space mechanisms and other appliances, and their reliability analysis has drawn more and more attention. This paper focuses on the performance degradation analysis of solid lubricated bearings. Based on the vibration and friction torque signal of solid lubricated bearings, Laplace wavelet filter is adopted to process vibration signal and feature vector is constructed by calculating time-domain parameters of filtered vibration signal and original friction torque signal. Self-organizing map is then adopted to analyze the performance degradation based on extracted feature vectors. Experimental results show that this method can describe performance degradation process effectively.

scholarly journals Engine Fault Detection Approach Based on Angle Domain Signal Model

2021 ◽  
Vol 2068 (1) ◽  
pp. 012034
Author(s):  
Hai Zeng ◽  
Ning Zeng ◽  
Jin Han ◽  
Yan Ding
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Failure Detection ◽  
Vibration Signal ◽  
Bench Test ◽  
Signal Model ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Detection Approach ◽  
Diagnosis Approach

Abstract Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain signal model involves equal angle sampling and envelope analysis of engine vibration signals. The engine bench test verifies the effectiveness of the engine fault diagnosis approach based on the angle domain signal model. In addition, this approach indicates a new path of engine fault diagnosis and detection.

scholarly journals Recent Advances in Non‐nucleophilic Mg Electrolytes

2021 ◽  
Vol 7 ◽  
Author(s):  
Qijie Wu ◽  
Kewei Shu ◽  
Lili Sun ◽  
Haihua Wang
Keyword(s):  
High Performance ◽  
Early Stage ◽  
Oxidation Stability ◽  
Short Review ◽  
High Reactivity ◽  
Inherent Safety ◽  
Practical Application ◽  
Safety Issues ◽  
Ion Structure ◽  
Recent Advances

High-performance electrolyte is still a roadblock for the development of rechargeable magnesium (Mg) batteries. Grignard-type electrolytes were once the only choice in the early stage of rechargeable Mg batteries research. However, due to their nucleophilic nature and high reactivity, Grignard-type electrolytes have inherent safety issues and low oxidation stability, which restrict the development of rechargeable Mg batteries in terms of practical application. Recently, emerging novel Mg battery systems such as Mg-S, Mg-O2/air batteries also require non‐nucleophilic electrolytes with high oxidation stability. This short review summarizes recent advances in non‐nucleophilic Mg electrolytes and aims to provide insights into electrochemical properties and active Mg ion structure of such electrolytes.

Mine Ventilator Fault Diagnosis Based on Harmonic Wavelet Analysis

2011 ◽  
Vol 143-144 ◽  
pp. 613-617
Author(s):  
Shuang Xi Jing ◽  
Yong Chang ◽  
Jun Fa Leng
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Wavelet Function ◽  
Frequency Region ◽  
Analysis Method ◽  
Vibration Signals ◽  
Harmonic Wavelet ◽  
Frequency Components

Harmonic wavelet function, with the strict box-shaped characteristic of spectrum, has strong ability of identifying signal in frequency domain, and can extract weak components form vibration signals in frequency domain. Using harmonic wavelet analysis method, the selected frequency region and other frequency components of vibration signal of mine ventilator were decomposed into independent frequency bands without any over-lapping or leaking. Simulation and diagnosis example show that this method has good fault diagnosis effect, and the ventilator fault is diagnosed successfully.

A fractal-dimension-based envelope demodulation for rolling element bearing fault feature extraction from vibration signals

2016 ◽  
Vol 230 (18) ◽  
pp. 3194-3211 ◽  
Author(s):  
Juanjuan Shi ◽  
Ming Liang
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
Search Algorithm ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Condition Monitoring ◽  
Interference Frequency ◽  
Fault Feature Extraction

Vibration analysis has been extensively used as an effective tool for bearing condition monitoring. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault feature (i.e. fault-induced impulses), periodic interferences from other mechanical/electrical components, and background noise. The incipient impulses which excite as well as modulate the resonance frequency of the system are easily masked by compounded effects of periodic interferences and noise, making it challenging to do a reliable fault diagnosis. As such, this paper proposes an envelope demodulation method termed short time fractal dimension (STFD) transform for fault feature extraction from such vibration signal mixture. STFD transform calculation related issues are first addressed. Then, by STFD, the original signal can be quickly transformed into a STFD representation, where the envelope of fault-induced impulses becomes more pronounced whereas interferences are partly weakened due to their morphological appearance differences. It has been found that the lower the interference frequency, the less effect the interference has on STFD representations. When interference frequency keeps increasing, more effects on STFD representations will be resulted. Such effects can be reduced by the proposed kurtosis-based peak search algorithm (KPSA). Therefore, bearing fault signature is kept and interferences are further weakened in the STFD-KPSA representation. The proposed method has been favourably compared with two widely used enveloping methods, i.e. multi-morphological analysis and energy operator, in terms of extracting impulse envelopes from vibration signals obscured by multiple interferences. Its performance has also been examined using both simulated and experimental data.

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