Xiaoliang Zhu

Improving Sensitivity of an Inductive Pulse Sensor for Detection of Metallic Wear Debris in Lubricants Using Parallel LC Resonance Method

Detection of small metallic wear debris is critical to identify abnormal wear conditions for prognosis of pending machinery failure. In this paper we applied an inductance–capacitance (LC) resonance method to an inductive pulse debris sensor to increase the sensitivity. By adding an external capacitor to the sensing coil of the sensor, a parallel LC resonance circuit is formed that has a unique resonant frequency. At an excitation frequency close to the resonant frequency, impedance change (and thus change in voltage output) of the LC circuit caused by the passage of a debris particle is amplified due to sharp change in impedance at the resonant peak; thus signal-to-noise ratio and sensitivity are significantly improved. Using an optimized measurement circuit, iron particles ranging from 32 to 96 µm and copper particles ranging from 75 to 172 µm were tested. Results showed that the parallel LC resonance method is capable of detecting a 20 µm iron particle and a 55 µm copper particle while detection limits for the non-resonance method are 45 and 125 µm, respectively. In contrast to the non-resonant method, the sensitivity of the resonance method has been significantly improved.

An Integrated Lubricant Oil Conditioning Sensor Using Signal Multiplexing

One effective approach to detect signs of potential failure of a rotating or reciprocating machine is to examine the conditions of its lubrication oil. Here we present an integrated oil condition sensor for detecting both wear debris and lubricant properties. The integrated sensor consists of miniature multiplexed sensing elements for detection of wear debris and measurements of viscosity and moisture. The oil debris sensing element consists of eight sensing channels to detect wear debris in parallel; the elements for measuring oil viscosity and moisture, based on interdigital electrode sensing, were fabricated using micromachining. The integrated sensor was installed and tested in a laboratory lubricating system. Signal multiplexing was applied to the outputs of the three sensing elements such that responses from all sensing elements were obtained within two measurements, and the signal-to-noise ratio was improved. Testing results show that the integrated sensor is capable of measuring wear debris (>50 µm), moisture (>50 ppm) and viscosity (>12.4 cSt) at a high throughput (200 ml min−1). The device can be potentially used for online health monitoring of rotating machines.

A high sensitivity inductive sensor for blade tip clearance measurement

A multiplexed inductive sensor consisting of multiple mini-sized planar spiral coils for detecting multiple tip clearances of rotor blades is presented. The sensor measures the tip clearances by monitoring the inductance changes of planar spiral coils caused by the passage of the rotor blades. A resonance frequency division multiplexing technique and parallel LC resonance measurement were applied to the multiple sensor coils, making it feasible to measure multiple tip clearances using only one set of measurement electronics with high sensitivity and resolution. The results from tests conducted on a bench-top test rig have demonstrated that the sensor is capable of simultaneously measuring multiple tip clearances from 0 to 5 mm with a 10 ?m resolution at a high rotary speed up to 80?000 RPM. With its high resolution, high sensitivity and capability of monitoring a large number of tip clearances simultaneously, this sensor can potentially be used for advanced active tip clearance control in turbine machinery.

An Inductive Sensor for Real-Time Measurement of Plantar Normal and Shear Forces Distribution

Goal: The objective of this paper is to demonstrate a multiplexed inductive force sensor for simultaneously measuring normal force and shear forces on a foot. Methods: The sensor measures the normal force and shear forces by monitoring the inductance changes of three planar sensing coils. Resonance frequency division multiplexing was applied to signals from the multiple sensing coils, making it feasible to simultaneously measure the three forces (normal force, shear forces in x- and y-axis) on a foot using only one set of measurement electronics with high sensitivity and resolution. Results: The testing results of the prototype sensor have shown that the sensor is capable of measuring normal force ranging from 0 to 800 N and shear forces ranging from 0 to 130 N in real time. Conclusion: With its high resolution, high sensitivity, and the capability of monitoring forces at different positions of a foot simultaneously, this sensor can be potentially used for real-time measurement of plantar normal force and shear forces distribution on diabetes patients foot. Significance: Real-time monitoring of the normal force and shear forces on diabetes patients foot can provide useful information for physicians and diabetes patients to take actions in preventing foot ulceration.

A microsensor array for quantification of lubricant contaminants using a back propagation artificial neural network

We present a method based on an electrochemical sensor array and a back propagation artificial neural network for detection and quantification of four properties of lubrication oil, namely water (0, 500 ppm, 1000 ppm), total acid number (TAN) (13.1, 13.7, 14.4, 15.6 mg KOH g−1), soot (0, 1%, 2%, 3%) and sulfur content (1.3%, 1.37%, 1.44%, 1.51%). The sensor array, consisting of four micromachined electrochemical sensors, detects the four properties with overlapping sensitivities. A total set of 36 oil samples containing mixtures of water, soot, and sulfuric acid with different concentrations were prepared for testing. The sensor arrays responses were then divided to three sets: training sets (80% data), validation sets (10%) and testing sets (10%). Several back propagation artificial neural network architectures were trained with the training and validation sets; one architecture with four input neurons, 50 and 5 neurons in the first and second hidden layer, and four neurons in the output layer was selected. The selected neural network was then tested using the four sets of testing data (10%). Test results demonstrated that the developed artificial neural network is able to quantitatively determine the four lubrication properties (water, TAN, soot, and sulfur content) with a maximum prediction error of 18.8%, 6.0%, 6.7%, and 5.4%, respectively, indicting a good match between the target and predicted values. With the developed network, the sensor array could be potentially used for online lubricant oil condition monitoring.