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

Detection and Diagnosis of Compound Faults in a Reciprocating Compressor based on Motor Current Signatures

Induction motors are the most common driver in the industry and consume tremendous energy every year. Monitoring the status of a motor and its downstream equipment and diagnosing faults in time not only avoids great damage to mechanical systems but also allows the motor to run at optimal efficiency. This paper studies the use of information from motor current signals to detect and diagnose faults of a reciprocating compressor (RC) and its upstream three-phase motor. The motor is applied by the RC with an oscillator torque which induces additional components in measured current signals. Moreover, the current signatures contain changes with the torque profiles due to different types of faults. Based on these analytical studies, experimental studies were carried out for different common RC faults, such as valve leakage, intercooler leakage, stator asymmetries and the compounds of them. The envelope analysis of current signals allows accurate demodulation of the torque profiles and thereby it can be combined with overall current levels for implementing model based detections and diagnosis. The results show these simulated faults can be separated under all operating pressures

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

The Real-time Implementation of Envelope Analysis for Bearing Fault Diagnosis based on Wireless Sensor Network

-Wireless sensor network (WSN) is gaining popularity in condition monitoring (CM) fields. However, enormous datasets obtained by data acquisition systems with high sampling rate cannot be transmitted effectively via a WSN due to its limited transmission speed. This may lead to the loss of significant information for condition monitoring and fault diagnosis, hence affecting the accuracy of diagnostic results. Local processing can be employed to improve the transmission efficiency; in that the acquired data is processed to extract the features locally for data compaction, and then only the analysis result is sent through the WSN. Therefore, the transmission load is reduced significantly and all the useful information is guaranteed to be transmitted for examining accurate detection results. However, the commercially available wireless sensors are usually not competent enough to fulfill complicated signal processing algorithms. In this paper, an intelligent WSN node capable of signal processing is proposed to improve the transmission efficiency of the WSN system and the envelope analysis algorithm is implemented on the sensor for bearing fault diagnostics. A bearing test rig is set up to verify the performance of this design. According to the test results, if a band-pass filter with 1000 Hz bandwidth is applied in the envelope analysis process, the data to be transmitted could be reduced by nearly 95%, which will make the real-time transmission effectively

Sistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBee

La inclusión de las redes inalámbricas de sensores y tecnologías IoT (Internet of Things) en ambientes industriales busca el monitoreo y registro autónomo de una mayor cantidad de variables del proceso industrial con una alta confiabilidad y resiliencia, además, procuran realizar un análisis previó para obtener parámetros de las señales que puedan dar a conocer una mejor descripción del estado del sistema y su condición de operación. Esto permite reducir el consumo de energía al disminuir la transmisión de datos netos medidos con paquetes hasta mil veces más largas que un parámetro calculado desde el sensor hacia los centros de control. La finalidad del monitoreo propuesto es el análisis para la identificación de anomalías que puedan afectar la disponibilidad de la planta o incrementar los costos de producción, y mejorar los procesos de mantenimiento. En este proyecto se desarrolló un sistema de monitoreo y diagnóstico remoto basado en una red de sensores 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 sistema propuesto se enfocó en el diagnóstico de falla de motores de inducción debido a que representan el mayor porcentaje de equipos en aplicaciones industriales. El proyecto se limitó a la identificación de falla entre espiras (2, 4 y 6 espiras), como un antecedente de fallas críticas, corto circuito fase-fase y corto circuito fase-tierra al presentarse como un deterioro del aislamiento. Se empleo el método de análisis de corriente de estator (MCSA). El 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 (gateway) está compuesto de una tarjeta Raspberrry PI con comunicación mediante protocolo HTTP y formato JSON (PI Web API) a la base de datos del sistema de monitoreo industrial PI System El diagnóstico se ejecuta de manera remota por medio de un análisis preliminar para el cálculo de indicadores de falla y luego mediante SVM (Support Vector Machine) se clasifican los datos en comportamientos conocidos de condiciones de falla. Se plantearon indicadores basados en la medición neta de las corrientes, FFT (Fast Fourier Transform) y DWT (Discrete Wavelet Transform). Se realizó validación en laboratorio de la clasificación en tiempo real de fallas entre espiras aplicadas a un motor de inducción tipo jaula de ardilla, comparando diferentes configuraciones del diagnóstico, del análisis para la extracción de indicadores y de los indicadores de falla empleados; permitiendo plantear mejoras para la reducción de los porcentajes de error por falsa detección de falla, o no detección de falla. Estos avances finalmente se traducen a incrementar la confiabilidad del diagnóstico, la observabilidad de la falla, la diferenciación entre condiciones de falla, la precisión de la clasificación, la tolerancia a transitorios, sensibilidad, entre otros.The inclusion of sensors wireless networks and Internet of Things (IoT) technologies in industrial environments seeks an autonomous monitoring and storage with high reliability and resilience of a greater number of industrial process variables, in addition, they attempt to perform a preliminary analysis to obtain parameters of the signals that can give a better description of system state and its operation condition. This allows reducing energy consumption by decreasing the transmission of raw data, a parameter calculated from the sensor to the control centers in change of a thousand times longer package. The purpose of the proposed monitoring is the analysis for the identification of anomalies that may affect the availability of the plant or increase production costs and improve maintenance processes. In this project, a remote fault diagnosis and monitoring system based on wireless sensor networks was developed whose remote nodes are responsible for data collection and analysis for the identification of anomalies over industrial process or system, previously to critical faults. The proposed system was focused on the induction motor fault diagnosis because these represent the highest percentage of equipment in industrial applications. This project was based on identify interturn faults (2, 4 and 6 turns) using Motor Current Signature Analysis (MCSA), because of the Interturn faults are produced by insulation deterioration and evolve in critical faults, phase to phase short-circuit and ground fault. The developed intelligent remote node was implemented with MCU LPCXpresso54114 with connection to a ZigBee protocol wireless sensor network through XBee communication module. The gateway node is a Raspberrry PI with communication through HTTP protocol and JSON format (PI Web API) to the PI System database (industrial monitoring system). The diagnosis is remotely executed, where a preliminary analysis is applied to calculate fault indicators. Then, with a SVM (Support Vector Machine), the data are classified in known behavior of fault conditions. Different fault indicators were proposed based on current measurement’s raw data, FFT (Fast Fourier Transform) and DWT (Discrete Wavelet Transform). Real time interturn fault classification was validated in laboratory with a squirrel cage induction motor comparing different settings and configuration of diagnosis, analysis for indicators extraction and testing diversified fault indicators. This allowed proposing improvements to reduce of error percentage by false detection or missing detection. The progress finally are reflected in increase the diagnosis reliability, the observability of the failure, the differentiation between fault conditions, classification accuracy, tolerance to transients, sensitivity, etc.Magíster en Ingeniería Eléctrica.Maestrí

The Use of Advanced Soft Computing for Machinery Condition Monitoring

The demand for cost effective, reliable and safe machinery operation requires accurate fault detection and classification. These issues are of paramount importance as potential failures of rotating and reciprocating machinery can be managed properly and avoided in some cases. Various methods have been applied to tackle these issues, but the accuracy of those methods is variable and leaves scope for improvement. This research proposes appropriate methods for fault detection and diagnosis. The main consideration of this study is use Artificial Intelligence (AI) and related mathematics approaches to build a condition monitoring (CM) system that has incremental learning capabilities to select effective diagnostic features for the fault diagnosis of a reciprocating compressor (RC). The investigation involved a series of experiments conducted on a two-stage RC at baseline condition and then with faults introduced into the intercooler, drive belt and 2nd stage discharge and suction valve respectively. In addition to this, three combined faults: discharge valve leakage combined with intercooler leakage, suction valve leakage combined with intercooler leakage and discharge valve leakage combined with suction valve leakage were created and simulated to test the model. The vibration data was collected from the experimental RC and processed through pre-processing stage, features extraction, features selection before the developed diagnosis and classification model were built. A large number of potential features are calculated from the time domain, the frequency domain and the envelope spectrum. Applying Neural Networks (NNs), Support Vector Machines (SVMs), Relevance Vector Machines (RVMs) which integrate with Genetic Algorithms (GAs), and principle components analysis (PCA) which cooperates with principle components optimisation, to these features, has found that the features from envelope analysis have the most potential for differentiating various common faults in RCs. The practical results for fault detection, diagnosis and classification show that the proposed methods perform very well and accurately and can be used as effective tools for diagnosing reciprocating machinery failure

Exploration of a Condition Monitoring System for Rolling Bearing Based on a Wireless Sensor Network

In recent years, wireless sensor networks (WSN) have attracted attention in machine condition monitoring (CM) fields for a more efficient system based on the inherent advantages of WSN, including ease of installation and relocation, lower maintenance cost and the ability to be installed in places not easily accessible. As critical components of rotating machines, bearings account for more than 40% of the various types of failures, causing considerable unpredicted breakdowns of a plant. Thus, this thesis intends to develop a cost-effective and reliable wireless measurement system for rolling bearing condition monitoring. Based on the investigation of various wireless protocols, Zigbee has been taken as a the most promising candidate for establishing the wireless condition monitoring system as it can have an acceptable bandwidth at low power consumption. However, a comparison made between wired and wireless measurement system has found that the Zigbee based wireless measurement system is deficient in streaming long continuous data of raw vibration signals from typical application environment with inevitable ambient interference. As a result, data loss can happen from time to time. To solve this issue, an on-board processing scheme is proposed by implementing advanced signal processing algorithms on the sensor side and only transmitting the processed results with a much smaller data size via the wireless sensor network. On this basis, a wireless sensor node prototype based on the state-of-the-art Cortex-M4F is designed to embed customizable signal processing algorithms. As an extensively employed algorithm for bearing fault diagnosis, envelope analysis is chosen as the on-board signal processing algorithm. Therefore, the procedure of envelope analysis and considerations for implementing it on a memory limited embedded processor are discussed in detail. With the optimization, an automatic data acquisition mechanism is achieved, which combines Timer, ADC and DMA to reduce the interference of CPU and thus to improve the efficiency for intensive computation. A 2048-point envelope analysis in single floating point format is realized on the processor with only 32kB memory. Experimental evaluation results show that the embedded envelope analysis algorithm can successfully diagnose the simulated bearing faults and the data transmission throughput can be reduced by at least 95% per frame compared with that of the raw data; this allows a large number of sensor nodes to be deployed in the network for real time monitoring. Furthermore, a computation efficient amplitude based optimal band selection algorithm is proposed for choosing an optimal band-pass filter for envelope analysis. Requiring only a small number of arithmetical operations, it can be embedded on the wireless sensor node to yield the desired performance of bearing fault detection and diagnosis

Compound Fault Diagnosis of Centrifugal Pumps Using Vibration Analysis Techniques

Centrifugal pumps are widely used in many different industrial processes, such as power generation stations, chemical processing plants, and petroleum industries. The problem of failures in centrifugal pumps is a large concern due to its significant influence on such critical industries. Particularly, as the core, parts of a pump, bearings and the impellers are subject to different corrosions and their faults can cause major degradation of pump performances and lead to the breakdown of production. Therefore, an early detection of these types of faults would provide information to take timely preventive actions. This research investigates more effective techniques for diagnosing common faults of impellers and bearings with advanced signal analysis of surface vibration. As overall vibration responses contain a high level of broadband noises due to fluid cavities and turbulences, noise reduction is critical to developing reliable and accurate features. However, considering the modulation effect between the rotating shaft, vane passing components and any structural resonances, a modulation signal bispectrum (MSB) method is mainly used to extract these deterministic characteristics of modulations, which differs from previous researches in that the broadband vibration is often characterised with statistical methods, high frequency demodulation along spectrum analysis. Both theoretical analysis and experimental evaluation show that the diagnostic features developed by MSB allow impellers with inlet vane damages and exit vane faults to be identified under different operating conditions. It starts with an in-depth examination of the vibration excitation mechanisms associated with each type of common pump faults including impeller leakages, impeller blockages, bearing inner race defects and bearing outrace defects. Subsequently, fault diagnosis was carried out using popular spectrum and envelope analysis, and more advanced kurtogram and MSB analysis. These methods all can successfully provide correct detection and diagnosis of the faults, which are induced manually to the test pump. Envelope analysis in a bands optimised with Kurtogram produces outstanding detection results for bearing faults but not the impeller faults in a frequency range as high as several thousand hertz (about 7.5kHz). In addition, it cannot provide satisfactory diagnostic results in separating the faults across different flow rates, especially when the compound faults were evaluated. This deficiency is because they do not have the capability of suppressing the random noises. Meanwhile, it has found that the MSB analysis allows both impeller and bearing faults to be detected and diagnosed. Especially, when the pump operated with compound faults both the fault types and severity can be attained by the analysis with acceptable accuracy for different flow rates. This high performance of diagnosis is due to that MSB has the unique capability of noise reduction and nonlinearity demodulation. Moreover, MSB diagnosis can be a frequency range lower than 2 times of the blade pass frequency (<1kHz), meaning that it can be more cost-effective as it demands lower performance measurement systems. In addition, the study also found that one accelerometer mounted on the pump housing is sufficient to monitor the faults on both the impeller and the bearing as it uses a lower frequency vibration which propagates far away from the bearing to the housing, rather than another accelerometer on the bearing pedestal directly