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 investigation of motor current signals from a centrifugal pump for fault diagnosis

In this paper, motor current signals from electrical control systems, rather than installing additional measurement systems, are characterised for the fault diagnosis of centrifugal pumps. Modulation signal bispectrum (MSB) analysis is applied to reveal the weak nonlinear characteristics of current signals when the pump with different impeller faults operates under a wide range of flow conditions. Experimental results show that two static features including the amplitude at supply frequency and the frequency value of bar-passing frequency can be based on to diagnose impeller defects on exit vane tips and inlet vane tips. In addition, the dynamic parameter of sidebands at vane-passing frequency can also be a good indicator for differentiating between the faults

Optimisation of vibration monitoring nodes in wireless sensor networks

This PhD research focuses on developing a wireless vibration condition monitoring (CM) node which allows an optimal implementation of advanced signal processing algorithms. Obviously, such a node should meet additional yet practical requirements including high robustness and low investments in achieving predictive maintenance. There are a number of wireless protocols which can be utilised to establish a wireless sensor network (WSN). Protocols like WiFi HaLow, Bluetooth low energy (BLE), ZigBee and Thread are more suitable for long-term non-critical CM battery powered nodes as they provide inherent merits like low cost, self-organising network, and low power consumption. WirelessHART and ISA100.11a provide more reliable and robust performance but their solutions are usually more expensive, thus they are more suitable for strict industrial control applications. Distributed computation can utilise the limited bandwidth of wireless network and battery life of sensor nodes more wisely. Hence it is becoming increasingly popular in wireless CM with the fast development of electronics and wireless technologies in recent years. Therefore, distributed computation is the primary focus of this research in order to develop an advanced sensor node for realising wireless networks which allow high-performance CM at minimal network traffic and economic cost. On this basis, a ZigBee-based vibration monitoring node is designed for the evaluation of embedding signal processing algorithms. A state-of-the-art Cortex-M4F processor is employed as the core processor on the wireless sensor node, which has been optimised for implementing complex signal processing algorithms at low power consumption. Meanwhile, an envelope analysis is focused on as the main intelligent technique embedded on the node due to the envelope analysis being the most effective and general method to characterise impulsive and modulating signatures. Such signatures can commonly be found on faulty signals generated by key machinery components, such as bearings, gears, turbines, and valves. Through a preliminary optimisation in implementing envelope analysis based on fast Fourier transform (FFT), an envelope spectrum of 2048 points is successfully achieved on a processor with a memory usage of 32 kB. Experimental results show that the simulated bearing faults can be clearly identified from the calculated envelope spectrum. Meanwhile, the data throughput requirement is reduced by more than 95% in comparison with the raw data transmission. To optimise the performance of the vibration monitoring node, three main techniques have been developed and validated: 1) A new data processing scheme is developed by combining three subsequent processing techniques: down-sampling, data frame overlapping and cascading. On this basis, a frequency resolution of 0.61 Hz in the envelope spectrum is achieved on the same processor. 2) The optimal band-pass filter for envelope analysis is selected by a scheme, in which the complicated fast kurtogram is implemented on the host computer for selecting optimal band-pass filter and real-time envelope analysis on the wireless sensor for extracting bearing fault features. Moreover, a frequency band of 16 kHz is analysed, which allows features to be extracted in a wide frequency band, covering a wide category of industrial applications. 3) Two new analysis methods: short-time RMS and spectral correlation algorithms are proposed for bearing fault diagnosis. They can significantly reduce the CPU usage, being over two times less and consequently much lower power consumptio

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

The Cognitive Perspective of Yulin Yuan on Modern Chinese Grammar

This paper is mainly a review of Yulin Yuan’s book Cognition-Based Studies on Chinese Grammar which, as one of the book series of Routledge Studies in Chinese Linguistics, was published by Routledge in 2017. On the one hand, Yuan’s cognitive studies of and his Yuanian insight into Chinese grammar are of vital importance to those students and researchers who specialise or are interested in the Chinese language, especially modern Chinese grammar. On the other hand, his research may probably promote the development of cognitive linguistics on the whole with regard to linguistic typology

Energy Harvesting Based Wireless Sensor Nodes for The Monitoring Temperature of Gearbox

Temperatures are effective indicators of the health of many ma-chines such as the wind turbine gearboxes, bearings, engines, etc. This paper pre-sents a novel wireless temperature sensor node powered by a thermal harvester for monitoring the status of gearboxes. A thermoelectric generator module (TEG) is optimized to harvest the electrical power from a heat source such as the gear-box undergoing such monitoring. The power generation from this method is ob-tained based on temperature gradients emanated by sandwiching the TEG be-tween the two aluminum plates. One plate is exposed to the heat source and has the role of a heat collector, whereas the other plate, mounted with a low profile heat-sink, acts as a heat spreader. The harvested power is then used to power a wireless temperature node for condition monitoring, resulting in a powerless and wireless monitoring system. To evaluate the system, an industrial gearbox is monitored by the designed temperature node. The node is fabricated using a TEG module; an LTC3108 DC-DC converter for boosting the voltage, a super-capacitor for energy storage and a CC2650 sensor tag for measuring the temperature of the gearbox. The temper-ature data is transferred via the Bluetooth Low Energy and then monitored using portable monitoring devices, such as a mobile phones. The results obtained show the system can provide a continuous monitoring of the temperature information

Application of Wavelet Packet Transform and Envelope Analysis to Non-stationary Vibration Signals For Fault Diagnosis of a Reciprocating Compressor

Reciprocating compressors play a major role in manufacturing industries such as oil and gas refineries, petrochemical industrial plants, etc. Therefore, it is necessary to implement online condition monitoring for early and accurate detection of faults which if not controlled can lead to machine inefficiency, damage, or total system shutdown. This paper presents the application of wavelet packet transform (WPT) and envelope analysis to non-stationary vibration signal from a two-stage reciprocating compressor for fault diagnostics. Vibration signal measured on the reciprocating compressor consist of a series of impulsive events with non-stationary random characteristics, which result mostly from mechanical impacts of the valves and impulsive fluid excitation of high-pressure turbulent flows. To characterize such vibration signal, WPT is employed to decompose the measured signal for the extraction of time-frequency information. With the help of statistical based analysis, the most optimal terminal node of the wavelet packet is selected for further study. Envelope spectrum of the optimal terminal node is processed and used for the classification of three common faults including intercooler leakage, second-stage discharge valve leakage and a combined fault at five critical tank discharge ranges (0.55, 0.62, 0.69, 0.76, and 0.83 MPa) for condition monitoring of a reciprocating compressor

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

Efficient implementation of envelope analysis on resources limited wireless sensor nodes for accurate bearing fault diagnosis

With the fast development of electronics and wireless communication technologies in recent years, intelligent wireless sensor nodes are becoming increasingly popular in the online machinery condition monitoring systems. They bring a number of benefits, such as reduced investment on the installation and maintenance of expensive communication cables, ease of deployment and upgrading. For the condition monitoring of dynamic signals, distributed computation on wireless sensor nodes is getting popular with wireless sensor nodes becoming more computation powerful and power efficient. As a widely recognised algorithm for bearing fault diagnosis, envelope analysis has been previously proved suitable for being embedded on the wireless sensor nodes to effectively extract fault features from common machinery components such as bearings and gears. As a continuation, this paper studies into several envelope detection methods, including Hilbert transform, spectral correlation, band-pass squared rectifier and short-time RMS. Regarding to the fact that only low frequency components in the bearing envelope is of interest, spectral correlation can be simplified for fast calculation and short-time RMS method can be considered as a simplified band-pass squared rectifier, in which partial aliasing is allowed. Thereafter, spectral correlation and short-time RMS are employed to speed up the calculation of envelope analysis on a wireless sensor node, which thereafter provides the potential to reduce power consumption of wireless sensor nodes. The computation speed comparison shows that the spectral correlation method and short-time RMS can speed up the computation speed by more than two times and five times in comparison with the Hilbert transform method. The simulation study shows that spectral correlation and short-time RMS based methods achieves similar level of accuracy as Hilbert transform. Furthermore, the experimental study shows that spectral correlation and short-time RMS based methods can well reveal the simulated three types of bearing faults while with the computation speed significantly improved