A Novel Method to Improve the Resolution of Envelope Spectrum for Bearing Fault Diagnosis Based on a Wireless Sensor Node

In this paper, an accurate envelope analysis algorithm is developed for a wireless sensor node. Since envelope signals employed in condition monitoring often have narrow frequency bandwidth, the proposed algorithm down-samples and cascades the analyzed envelope signals to construct a relatively long one. Thus, a relatively higher frequency resolution can be obtained by calculating the spectrum of the cascaded signal. In addition, a 50 % overlapping scheme is applied to avoid the distortions caused by Hilbert transform based envelope calculation. The proposed method is implemented on a wireless sensor node and tested successfully for detecting an outer race fault of a rolling bearing. The results show that the frequency resolution of the envelope spectrum is improved by 8 times while the data transmission remains at a low rate

Remote online machine condition monitoring using advanced internet, wireless and mobile communication technologies

A conceptual model with wireless and mobile techniques is developed in this thesis for remote real-time condition monitoring, which is applied for monitoring, diagnosing, and controlling the working conditions of machines. The model has the following major functions: data acquisition, data processing, decision making, and remote communication. The data acquisition module is built up within this model using the sensory technique and data I/O interfaces to acquire the working conditions data of a machine and extract the physical information about the machine (e.g. failure, wear, etc.) for data processing and decision making. The data processing is conducted using digital conversion and feature extraction to process the received analogue condition data and convert the data into the physical quantities of working condition of the machine for sequent fault diagnosis. A real-time fault diagnostic scheme for decision-making is applied based on digital filtering and pattern classification to real-time identify the fault symptom of the machine and provide advice for decision making for maintenance. Process control is implemented to control the operation status of the machine automatically, inform the maintenance personnel diagnostic results and alert the working conditions of the machine. Remote communication with wireless and mobile features greatly advance the machine’s condition monitoring technology with real-time fault diagnostic capacity, by providing a wireless-based platform to enable the implementation of data acquisition, real-time fault diagnosis, and decision making through the Internet, wireless, and mobile phone network. The model integrating above techniques and methods has been applied into the following three areas: (1) Development of a Remote Real-time Condition Monitoring System of Industrial Gearbox, supported by the Stimulation Innovation Success programme (2007-2008); (2) Development of a Remote Control System of Solid Desiccant Dehumidifier for Air Conditioning in Low Carbon Emission Buildings, supported by the Sustainable Construction iNET programme (2009-2010); (3) Development of an Innovative Remote Monitoring System of Thermo-Electric-Generations, supported by the Sustainable Construction iNET programme (2010-2011). The combination of wireless and mobile techniques with data acquisition, real-time fault diagnosis, and decision-making, into a model for remote real-time condition monitoring is a novel contribution to this area

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Wind Turbine Fault Detection System in Real Time Remote Monitoring

In new energy development, wind power has boomed. It is due to theproliferation of wind parks and their operation in supplying the nationalelectric grid with low cost and clean resources. Hence, there is an increasedneed to establish a proactive maintenance for wind turbine machines based on remote control and monitoring. That is necessary with a real-time wireless connection in offshore or inaccessible locations while the wired method has many flaws. The objective of this strategy is to prolong wind turbine lifetime and to increase productivity. The hardware of a remote control and monitoring system for wind turbine parks is designed. It takes advantage of GPRS or Wi-Max wireless module to collect data measurements from different wind machine sensors through IP based multi-hop communication. Computer simulations with Proteus ISIS and OPNET software tools have been conducted to evaluate the performance of the studied system. Study findings show that the designed device is suitable for application in a wind park.DOI:http://dx.doi.org/10.11591/ijece.v4i6.647

Sistema de diagnóstico distribuido de fallas basado en redes inalámbricas de sensores

This article presents the development of a distributed fault diagnosis and monitoring system whose remote nodes are responsible for data collection and distributed analysis to identify problems that could lead to critical faults in industrial processes or systems. The developed intelligent remote node was implemented with MCU LPCXpresso54114 connected to a ZigBee protocol wireless sensor network through XBee communication module. The gateway node is a Raspberrry PI with HTTP communication and JSON format to the PI System industrial monitoring system database. Motor Current Signature Analysis (MCSA) was implemented and validated to identify interturn faults of induction motors. The developed platform is a tool to perform comparison and validation of analysis techniques, indicators, and fault classification, because there are different combinations that can be applied to improve diagnosis reliability, fault observability, differentiation between fault conditions, classification accuracy, tolerance to transients, sensitivity, among others.En este artículo presenta el desarrollo de un sistema de monitoreo y diagnóstico distribuido cuyos nodos remotos se encarguen de la recolección de datos y su posterior análisis para la identificación de anomalías que representen fallas críticas para el proceso o sistema industrial. El dispositivo desarrollado como nodo remoto inteligente se implementó con MCU LPCXpresso54114 con conexión a una red inalámbrica de sensores basada en protocolo ZigBee mediante tarjetas de comunicación XBee. El nodo concentrador está compuesto de una tarjeta Raspberrry PI con comunicación mediante protocolo HTTP y formato JSON a la base de datos del sistema de monitoreo industrial PI System. Se implementó y validó el acondicionamiento de señal para la medición de corrientes de estator (MCSA) que permitió identificar fallas entre espiras de motores de inducción tipo jaula de ardilla. La plataforma presentada finalmente es una herramienta para realizar comparación y validación de técnicas de análisis, indicadores y de clasificación de fallas, puesto que existen diversas combinaciones que pueden ser aplicadas con el fin de mejorar la confiabilidad del diagnóstico, la observación de la falla, la diferenciación entre condiciones de falla, la precisión de la clasificación, la tolerancia a transitorios, sensibilidad, entre otros

Design of a Wireless Sensor Node for Vibration Monitoring of Industrial Machinery

Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis

Wireless fault tolerances decision using artificial intelligence technique

Wireless techniques utilized in industrial applications face significant challenges in preventing noise, collision, and data fusion, particularly when wireless sensors are used to identify and classify fault in real time for protection. This study will focus on the design of integrated wireless fault diagnosis system, which is protecting the induction motor (IM) from the vibration via decrease the speed. The filtering, signal processing, and Artificial Intelligent (AI) techniques are applied to improve the reliability and flexibility to prevent vibration increases on the IM. Wireless sensors of speed and vibration and card decision are designed based on the wireless application via the C++ related to the microcontroller, also, MATLAB coding was utilized to design the signal processing and the AI steps. The system was successful to identify the misalignment fault and dropping the speed when vibrations rising for preventing the damage may be happen on the IM. The vibration value reduced via the system producing response signal proportional with fault values based on modify the main speed signal to dropping the speed of IM