Application of Improved Jellyfish Search algorithm in Rotate Vector reducer fault diagnosis

In order to overcome the low accuracy of traditional Extreme Learning Machine (ELM) network in the performance evaluation of Rotate Vector (RV) reducer, a pattern recognition model of ELM based on Ensemble Empirical Mode Decomposition (EEMD) fusion and Improved artificial Jellyfish Search (IJS) algorithm was proposed for RV reducer fault diagnosis. Firstly, it is theoretically proved that the torque transmission of RV reducer has periodicity during normal operation. The characteristics of data periodicity can be effectively reflected by using the test signal periodicity characteristics of rotating machinery and EEMD. Secondly, the Logistic chaotic mapping of population initialization in JS algorithm is replaced by tent mapping. At the same time, the competition mechanism is introduced to form a new IJS. The simulation results of standard test function show that the new algorithm has the characteristics of faster convergence and higher accuracy. The new algorithm was used to optimize the input layer weight of the ELM, and the pattern recognition model of IJS-ELM was established. The model performance was tested by XJTU-SY bearing experimental data set of Xi'an Jiaotong University. The results show that the new model is superior to JS-ELM and ELM in multi-classification performance. Finally, the new model is applied to the fault diagnosis of RV reducer. The results show that the proposed EEMD-IJS-ELM fault diagnosis model has higher accuracy and stability than other models

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Vibration Monitoring: Gearbox identification and faults detection

L'abstract è presente nell'allegato / the abstract is in the attachmen

Mining Technologies Innovative Development

The present book covers the main challenges, important for future prospects of subsoils extraction as a public effective and profitable business, as well as technologically advanced industry. In the near future, the mining industry must overcome the problems of structural changes in raw materials demand and raise the productivity up to the level of high-tech industries to maintain the profits. This means the formation of a comprehensive and integral response to such challenges as the need for innovative modernization of mining equipment and an increase in its reliability, the widespread introduction of Industry 4.0 technologies in the activities of mining enterprises, the transition to "green mining" and the improvement of labor safety and avoidance of man-made accidents. The answer to these challenges is impossible without involving a wide range of scientific community in the publication of research results and exchange of views and ideas. To solve the problem, this book combines the works of researchers from the world's leading centers of mining science on the development of mining machines and mechanical systems, surface and underground geotechnology, mineral processing, digital systems in mining, mine ventilation and labor protection, and geo-ecology. A special place among them is given to post-mining technologies research

The Untilisation of Information Available in a Sensorless Control System of an AC Induction Motor for Condition Monitoring

Induction motor driven mechanical transmission systems are widely utilised in many applications across numerous sectors including industry, power generation and transportation. They are however subject to common failure modes primarily associated with faults in the driven mechanical components. Notably, gearboxes, couplings and bearings can cause significant defects in both the electrical and mechanical systems. Condition monitoring (CM) undertakes a key role in the detection of potential defects in the early development stages and in turn avoiding catastrophic operational and financial consequences caused by unplanned breakdowns. Meanwhile, variable speed drives (VSDs) have been increasingly deployed in recent years to achieve accurate speed control and higher operational efficiency. Among the different speed control designs, sensorless VSDs deliver improved dynamic performance and obviate speed measurement devices. This solution however results in heightened noise levels and continual changes in the power supply parameters that potentially impede the detection of minute fault features. This study addresses the gap identified through a systematic review of the literature on the monitoring of mechanical systems utilise induction motors (IM) with sensorless VSDs. Specifically, existing techniques prove ineffective for common mechanical faults that develop in gearboxes and friction induced scenarios. The primary aim of this research centres on the development of a more effective and accurate diagnostic solution for VSD systems using the data available in a VSD. An experimental research approach is based to model and simulate VSD systems under different fault conditions and gather in-depth data on changes in electrical supply parameters: current, voltage and power. Corresponding techniques including model based methods and dynamic signature analysis methods were developed for extracting the changes from noise measurements. An observer based detection technique is developed based on speed and flux observers that are deployed to generate power residuals. Both static and dynamic techniques are incorporated for the first time in order to detect the mechanical misalignment and lubrication degradation, each with different degrees of severities. The results of this study demonstrate that observer based approaches utilising power residual signalling can be effective in the identification of different faults in the monitoring of sensorless VSD driven mechanical systems. Specifically, the combination between dynamic and static components of the power supply parameters and control data has proved effective in separating the four types of common faults: shaft misalignment, lubricant shortage, viscosity changes and water contamination. The static data based approach outperforms the dynamic data based approach in detecting shaft misalignments under sensorless operating modes. The dynamic components of power signals, however, records superior results in the detection of different oil degradation problems. Nevertheless, static components of torque related variables, power and voltage can be used jointly in separating the three tested lubricant faults

Advanced Algorithms for Automatic Wind Turbine Condition Monitoring

Reliable and efficient condition monitoring (CM) techniques play a crucial role in minimising wind turbine (WT) operations and maintenance (O&M) costs for a competitive development of wind energy, especially offshore. Although all new turbines are now fitted with some form of condition monitoring system (CMS), very few operators make use of the available monitoring information for maintenance purposes because of the volume and the complexity of the data. This Thesis is concerned with the development of advanced automatic fault detection techniques so that high on-line diagnostic accuracy for important WT drive train mechanical and electrical CM signals is achieved. Experimental work on small scale WT test rigs is described. Seeded fault tests were performed to investigate gear tooth damage, rotor electrical asymmetry and generator bearing failures. Test rig data were processed by using commercial WT CMSs. Based on the experimental evidence, three algorithms were proposed to aid in the automatic damage detection and diagnosis during WT non-stationary load and speed operating conditions. Uncertainty involved in analysing CM signals with field fitted equipment was reduced, and enhanced detection sensitivity was achieved, by identifying and collating characteristic fault frequencies in CM signals which could be tracked as the WT speed varies. The performance of the gearbox algorithm was validated against datasets of a full-size WT gearbox, that had sustained gear damage, from the National Renewable Energy Laboratory (NREL) WT Gearbox Condition Monitoring Round Robin project. The fault detection sensitivity of the proposed algorithms was assessed and quantified leading to conclusions about their applicability to operating WTs

Hidden Markov model based rotate vector reducer fault detection using acoustic emissions

This paper proposes a hidden Markov model (HMM) based RV reducer fault detection using acoustic emission (AE) measurements. Compared with the conventional faults from the common rotating machinery (such as bearings and gears), faults from RV reducer are more complicated and undetectable due to its inherent inline and two-stage meshing structure. To this end, this work modifies the HMM model by taking into account not only the current observations and previous states, but the subsequent series of observations within posteriori probability framework. Through this way, the random and unknown disturbance could be suppressed. Besides, HMM is also applied to separate AE signal bulks within one cycle that has 39 subcycles. The proposed method has been evaluated on our collected AE signal dataset from the RV reducer in the industrial robotic platform. The experimental results and analysis validate the effectiveness and accuracy of our RV reducer fault detection model.</p

Hidden Markov model based rotate vector reducer fault detection using acoustic emissions

This paper proposes a hidden Markov model (HMM) based RV reducer fault detection using acoustic emission (AE) measurements. Compared with the conventional faults from the common rotating machinery (such as bearings and gears), faults from RV reducer are more complicated and undetectable due to its inherent inline and two-stage meshing structure. To this end, this work modifies the HMM model by taking into account not only the current observations and previous states, but the subsequent series of observations within posteriori probability framework. Through this way, the random and unknown disturbance could be suppressed. Besides, HMM is also applied to separate AE signal bulks within one cycle that has 39 subcycles. The proposed method has been evaluated on our collected AE signal dataset from the RV reducer in the industrial robotic platform. The experimental results and analysis validate the effectiveness and accuracy of our RV reducer fault detection model

Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment

To navigate autonomously in a manufacturing environment Automated Guided Vehicle (AGV) needs the ability to infer its pose. This paper presents the implementation of the Extended Kalman Filter (EKF) coupled with a feedforward neural network for the Visual Simultaneous Localization and Mapping (VSLAM). The neural extended Kalman filter (NEKF) is applied on-line to model error between real and estimated robot motion. Implementation of the NEKF is achieved by using mobile robot, an experimental environment and a simple camera. By introducing neural network into the EKF estimation procedure, the quality of performance can be improved

Prediction of Robot Execution Failures Using Neural Networks

In recent years, the industrial robotic systems are designed with abilities to adapt and to learn in a structured or unstructured environment. They are able to predict and to react to the undesirable and uncontrollable disturbances which frequently interfere in mission accomplishment. In order to prevent system failure and/or unwanted robot behaviour, various techniques have been addressed. In this study, a novel approach based on the neural networks (NNs) is employed for prediction of robot execution failures. The training and testing dataset used in the experiment consists of forces and torques memorized immediately after the real robot failed in assignment execution. Two types of networks are utilized in order to find best prediction method - recurrent NNs and feedforward NNs. Moreover, we investigated 24 neural architectures implemented in Matlab software package. The experimental results confirm that this approach can be successfully applied to the failures prediction problem, and that the NNs outperform other artificial intelligence techniques in this domain. To further validate a novel method, real world experiments are conducted on a Khepera II mobile robot in an indoor structured environment. The obtained results for trajectory tracking problem proved usefulness and the applicability of the proposed solution