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

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

The Harmonic Order Tracking Analysis (HOTA) for the Diagnosis of Induction Generators Working Under Steady State Regime

[EN] Improved fault diagnostic techniques in induction generators is a field of growing interest given the negative impact * that unexpected breakdowns have on energy production and on the electrical system. New diagnostic techniques based on induction generator currents monitoring have recently been developed, but their use is still irrelevant despite the advantages that presents to detect electrical faults in the generator. This situation is due to the needs of high computing power and memory resources which are not available in embedded devices for on-line monitoring, also, to the use of signal processing techniques that generate volumes of data difficult to transfer to control centres, where they could be processed. This paper proposes the use of a recent methodology known as the harmonic order tracking analysis (HOTA) that solve these problems to for the diagnosis of induction generators. This approach can be implemented in low cost digital devices; the resultant patterns are very simple and easily interpretable, even by nonqualified personnel. Moreover, these patterns are characterized by a very low number of parameters, which make easy their transmission to remote control centres. In this paper the practical application of this approach is proposed using a laboratory test bed.This work was supported by the Spanish "Ministerio de Economía y Competitividad" in the framework of the "Programa Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad¿ (project reference DPI2014-60881-R)Pérez-Cruz, J.; Pérez Vázquez, M.; Pineda-Sanchez, M.; Puche-Panadero, R.; Sapena-Bano, A. (2017). The Harmonic Order Tracking Analysis (HOTA) for the Diagnosis of Induction Generators Working Under Steady State Regime. DEStech Publications. 1864-1869. http://hdl.handle.net/10251/139470S1864186

An adaptive envelope analysis in a wireless sensor network for bearing fault diagnosis using fast kurtogram algorithm

This paper proposes a scheme to improve the performance of applying envelope analysis in a wireless sensor network for bearing fault diagnosis. The fast kurtogram is realized on the host computer for determining an optimum band-pass filter for the envelope analysis that is implemented on the wireless sensor node to extract the low frequency fault information. Therefore, the vibration signal can be monitored over the bandwidth limited wireless sensor network with both intelligence and real-time performance. Test results have proved that the diagnostic information for different bearing faults can be successfully extracted using the optimum band-pass filter

An investigation of the orthogonal outputs from an on-rotor MEMS accelerometer for reciprocating compressor condition monitoring

With rapid development in electronics and microelectromechanical systems (MEMS) technology, it becomes possible and attractive to monitor rotor dynamics by directly installing MEMS accelerometers on rotors. This paper studies the mathematical modelling of the orthogonal outputs from an on-rotor MEMS accelerometer and proposes a method to eliminate the gravitational acceleration projected on the measurement axes. This is achieved by shifting the output in the normal direction by π/2π/2 using a Hilbert transform and then combining it with the output of the tangential direction. With further compensation of the combined signal in the frequency domain, the tangential acceleration of the rotor is reconstructed to a high degree of accuracy. Experimental results show that the crankshaft tangential acceleration of a reciprocating compressor, obtained by the proposed method, can discriminate clearly between different discharge pressures and hence can allow common leakage faults to be detected, located and diagnosed for online condition monitoring purposes

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

An Intelligent Automated Method to Diagnose and Segregate Induction Motor Faults

In the last few decades, various methods and alternative techniques have been proposed and implemented to diagnose induction motor faults. In an induction motor, bearing faults account the largest percentage of motor failure. Moreover, the existing techniques related to current and instantaneous power analysis are incompatible to diagnose the distributed bearing faults (race roughness), due to the fact that there does not exist any fault characteristics frequency model for these type of faults. In such a condition to diagnose and segregate the severity of fault is a challenging task. Thus, to overcome existing problem an alternative solution based on artificial neural network (ANN) is proposed. The proposed technique is harmonious because it does not oblige any mathematical models and the distributed faults are diagnosed and classified at incipient stage based on the extracted features from Park vector analysis (PVA). Moreover, the experimental results obtained through features of PVA and statistical evaluation of automated method shows the capability of proposed method that it is not only capable enough to diagnose fault but also can segregate bearing distributed defects

A Magneto-Mechanical Piezoelectric Energy Harvester Designed to Scavenge AC Magnetic Field from Thermal Power Plant with Power-Line Cables

Piezoelectric energy harvesters have attracted much attention because they are crucial in portable industrial applications. Here, we report on a high-power device based on a magneto-mechanical piezoelectric energy harvester to scavenge the AC magnetic field from a power-line cable for industrial applications. The electrical output performance of the harvester (×4 layers) reached an output voltage of 60.8 Vmax, an output power of 215 mWmax (98 mWrms), and a power density of 94.5 mWmax/cm3 (43.5 mWrms/cm3) at an impedance matching of 5 kΩ under a magnetic field of 80 μT. The multilayer energy harvester enables high-output performance, presenting an obvious advantage given this improved level of output power. Finite element simulations were also performed to support the experimental observations. The generator was successfully used to power a wireless sensor network (WSN) for use on an IoT device composed of a temperature sensor in a thermal power station. The result shows that the magneto-mechanical piezoelectric energy harvester (MPEH) demonstrated is capable of meeting the requirements of self-powered monitoring systems under a small magnetic field, and is quite promising for use in actual industrial applications

Lifetime Maximization of Wireless Sensor Networks for a Fault Diagnosis System Using LEACH Protocol

The breakdown in a machine created by a fault will greatly affect the plant operation. The frame work of fault diagnosis of machines using machine learning techniques is an established area. Here, the fault diagnosis system is implemented with the help of wireless sensor networks (WSN). Each machine in the plant is fitted with sensors (node) from which the fault diagnosis is carried out. The signals/messages from each node are transmitted to a base station, which acts as a central control unit for the entire plant. Fault diagnosis using WSN in a factory setup has few challenges. The major issue in WSN is the life time of the nodes, as they are situated far away from base station in many plants like thermal power plant, refinery, and petroleum industries. To address the life time of the nodes many researchers have developed many protocols like LEACH, SEP, ERP, HCR, HEED, and PEGASIS. The plants need customisation in terms of choosing suitable algorithms and choosing location of the base station within the plant for better life time of the nodes. This paper presents results of both experimental and simulation studies of a typical plant, where the vibration signals from each machine are acquired and through machine learning techniques the fault diagnosis is performed with the help of wireless sensor networks. For illustrative purpose, a well reported bearing fault diagnosis data set is taken up and fault diagnosis case study was performed from wireless sensor networks point of view (experimental study). Here, at every stage, the computational time is taken as a primary concern which affects the life time of the sensor nodes. Then, the WSN of 18 sensor nodes representing 18 machines with LEACH protocol is simulated in Matlab© to study the life time characteristics of each node while keeping base station at different locations. The life time of different nodes is heavily dependent on the location of the base station. Finding the right location of the base station for a given plant is another contribution of this work