Analysis Of Head Length Effect Of Wire Rope Sensor On Output Voltage

Wire rope are used extensively in industrial applications involving lifting machines such as lift system, cable car system as well as crane services. In many cases, failure of a wire rope could lead to expensive damage to equipment or even to loss of life. Structural integrity of the wire rope is to be monitored to safe guard human lives. Regular periodic inspection is necessary for optimum safe utilization. Apart from visual inspection, the non destructive test methods are available to check the condition of this wire rope. Various methods have been used for wire rope testing such as eddy current, ultrasonic and radiographic. However, each method has some drawbacks in their application. Currently, there is no specific tool to design the wire rope sensor. Trial and error method was used to design the wire rope sensor and it will consume longer time for prototyping and fabricating the sensor. This method does not offer rapid performance evaluation of the designed sensor and it will cause the process of improving the efficiency of the sensor will be slower. Therefore, development of a model to design the wire rope sensor has been proposed. This model is able to investigate variable parameter involved in designing the wire rope sensor and it will speeding the process of prototyping the wire rope sensor. In this research, a fabricated wire rope sensor based on electromagnetism principles has been analyzed. The sensor applies the theory of magnetic circuit for the crack detection operation. This is an added feature to the sensor as the magnetic circuit does not need any energy supply to be energized which indirectly reduces its energy consumption. This sensor is a passive type sensor and its structure is very simple. It is made up of three main components; sensor head, center yoke rounded with copper wire and a set of permanent magnet. The sensor will only produce a signal when there is a relative movement between the sensor head and the tested wire rope. Derivations of theoretical calculations using permeance method was done to obtain a tools that manages to study the physical structures behaviors of the sensor. Finite Element Method (FEM) simulations and laboratory experiments have been conducted to observe the effects of head length to the output voltage of the sensor. The objective of this research is to perform the analysis of head length, Lh effect of wire rope sensor on output voltage was successfully achieved. A theoretical equation for the voltage induced by the sensor has been deduced using permeance method. Finite Element Method (FEM) simulation and laboratory experiments were done to observe the effects of head length of the sensor head to the output voltage of the sensor. Comparison between simulation result, theoretical calculation and laboratory experiment shows almost identical results. The analysis is necessary to obtain the best design for the wire rope sensor that would produce high output voltage

Fabrication of plasmonic thin film via DC sputtering with optics based assessment for trasmittance, absorbance and resonance

A plasmonic thin film is potentially to be used with the advancement in optical biosensor. It is a label free without a need of fluorescent, chemiluminescent, radioisotope and etc. It is crucial to design a low cost biosensor that is easily fabricated at precise sizes and density. This paper reported a fabrication for copper and gold thin film on a glass substrate with a magnetron sputtering. The objectives are to: 1-Fabricate the thin film, 2- Develop the optics setup, 3-Evaluate the thin films and 4-Exhibit the optical resonance. Seven glass slides were coated with six copper and remaining with gold at different sputtering time. The time was varied from 280 sec to 980 sec while Argon gas and DC power were maintained respectively at 80 sccm and 130 watt. Later, the optics based was employed for assessing the film thicknesses. The thin films fabrication indicates different thicknesses were achieved at various sputtering time. Given y is a thicknesses and x is a sputtering time, respectively the copper and gold thin film were changed quantitatively at y= 28.335e0.0005x and y= 0.25x. Qualitatively, spectral transmittance and absorbance were changed to the thicknesses of the thin films. The plasmonic resonance was achieved with gold thin film at 50 nm thicknesses. The resonance sensitivity was decreases as the thin films thicknesses were increases

ECG-based Detection and Prediction Models of Sudden Cardiac Death: Current Performances and New Perspectives on Signal Processing Techniques

Heart disease remains the main leading cause of death globally and around 50% of the patients died due to sudden cardiac death (SCD). Early detection and prediction of SCD have become an important topic of research and it is crucial for cardiac patient’s survival. Electrocardiography (ECG) has always been the first screening method for patient with cardiac complaints and it is proven as an important predictor of SCD. ECG parameters such as RR interval, QT duration, QRS complex curve, J-point elevation and T-wave alternan are found effective in differentiating normal and SCD subjects. The objectives of this paper are to give an overview of SCD and to analyze multiple important ECG-based SCD detection and prediction models in terms of processing techniques and performance wise. Detail discussions are made in four major stages of the models developed including ECG data, signal pre-processing and processing techniques as well as classification methods. Heart rate variability (HRV) is found as an important SCD predictor as it is widely used in detecting or predicting SCD. Studies showed the possibility of SCD to be detected as early as one hour prior to the event using linear and non-linear features of HRV. Currently, up to 3 hours of analysis has been carried out. However, the best prediction models are only able to detect SCD at 6 minutes before the event with acceptable accuracy of 92.77%. A few arguments and recommendation in terms of data preparation, processing and classification techniques, as well as utilizing photoplethysmography with ECG are pointed out in this paper so that future analysis can be done with better accuracy of SCD detection accuracy

Dual resonant frequencies effects on an induction-based oil palm fruit sensor

As the main exporter in the oil palm industry, the need to improve the quality of palm oil has become the main interest among all the palm oil millers in Malaysia. To produce good quality palm oil, it is important for the miller to harvest a good oil palm Fresh Fruit Bunch (FFB). Conventionally, the main reference used by Malaysian harvesters is the manual grading standard published by the Malaysian Palm Oil Board (MPOB). A good oil palm FFB consists of all matured fruitlets, aged between 18 to 21 weeks of antheses (WAA). To expedite the harvesting process, it is crucial to implement an automated detection system for determining the maturity of the oil palm FFB. Various automated detection methods have been proposed by researchers in the field to replace the conventional method. In our preliminary study, a novel oil palm fruit sensor to detect the maturity of oil palm fruit bunch was proposed. The design of the proposed air coil sensor based on the inductive sensor was further investigated mainly in the context of the effect of coil diameter to improve its sensitivity. In this paper, the sensitivity of the inductive sensor was further examined with a dual flat-type shape of air coil. The dual air coils were tested on fifteen samples of fruitlet from two categories, namely ripe and unripe. Samples were tested within 20 Hz to 10 MHz while evaluations on both peaks were done separately before the gap between peaks was analyzed. A comparative analysis was conducted to investigate the improvement in sensitivity of the induction-based oil palm fruit sensor as compared to previous works. Results from the comparative study proved that the inductive sensor using a dual flat-type shape air coil has improved by up to 167%. This provides an indication in the improvement in the coil sensitivity of the palm oil fruit sensor based on the induction concept

Relative Estimation of Water Content for Flat-Type Inductive-Based Oil Palm Fruit Maturity Sensor

The paper aims to study the sensor that identifies the maturity of oil palm fruit bunches by using a flat-type inductive concept based on a resonant frequency technique. Conventionally, a human grader is used to inspect the ripeness of the oil palm fresh fruit bunch (FFB) which can be inconsistent and inaccurate. There are various new methods that are proposed with the intention to grade the ripeness of the oil palm FFB, but none has taken the inductive concept. In this study, the resonance frequency of the air coil is investigated. Samples of oil palm FFB are tested with frequencies ranging from 20 Hz to 10 MHz and the results obtained show a linear relationship between the graph of the resonance frequency (MHz) against time (Weeks). It is observed that the resonance frequencies obtained for Week 10 (pre-mature) and Week 18 (mature) are around 8.5 MHz and 9.8 MHz, respectively. These results are compared with the percentage of the moisture content. Hence, the inductive method of the oil palm fruit maturity sensor can be used to detect the change in water content for ripeness detection of the oil palm FFB

Plasmonic wave assessment via optomechatronics system for biosensor application

Transduction biosensor (mass-based, optical and electrochemical) involves analysis, recognition and amplification in the acquired sample. In this work, the plasmonic-based biosensor was employed without using tags. It is crucial to determine angles of Brewster (Ɵb) and critical (Ɵc) for generating plasmonic resonance (Ɵr). The objective is to verify a cost-effective plasmonic biosensor through Fresnel simulation and experimentation of a developed optomechatronics system. The borosilicate glass, Au and Air layers were simulated with the Winspall 3.02 simulator. The optomechatronics system consists of: 1-optics (650 nm laser, slit, polarizer, photodiode), 2-mechanical (bipolar stepper motors, gears, stages) and 3-electronics (PIC18F4550, liquid crystal display (LCD) and drivers). Later, the software performs angular interrogation by reading the reflected beam from a rotating prism at 0.1125. Experimentation to simulation accuracy indicates that percentage differences for Ɵr and Ɵc are 1% and 0.2%, respectively. In conclusion, excellence verification was successfully achieved between experimentation and simulation. It proved that the low-cost optomechatronics system is capable and reliable to be deployed for the biosensor application

Machine learning approach for stress detection based on alpha-beta and theta-beta ratios of EEG signals

The contribution to stress detection and classification is far beyond demand as the statistics show that the health and mental illness of society have kept on deteriorating. Electroencephalogram (EEG) signals have the potential to detect stress levels reliably due to their high accuracy. Majority of studies of stress detection are based on alpha and beta waves and the corresponding ratio of the two waves and there are hardly any based-on theta waves. This work explores the impact of bandpower of alpha/beta and theta/beta ratios when combined with other features to classify two-levels of human stress based on EEG signals using five commonly used machine learning algorithms. A classification model is developed from the clustering model gained and Naïve Bayes shows the highest accuracy which is 95% in compared to the other four common machine learning algorithms (i.e., SVM, Logistic, IBk, and SGD) by using WEKA. The proposed framework recommends that both ratios are reliable features, and theta/beta appears to give a huge impact compared to alpha/beta. This study will ultimately contribute to society's development with improved robust machine learning algorithm for binary classification

Improvement in sensitivity of an inductive oil palm fruit sensor

Among palm oil millers, the ripeness of oil palm Fresh Fruit Bunch (FFB) is determined through visual inspection. To increase the productivity of the millers, many researchers have proposed with a new detection method to replace the conventional one. The sensitivity of such a sensor plays a crucial role in determining the effectiveness of the method. In our preliminary study a novel oil palm fruit sensor to detect the maturity of oil palm fruit bunches is proposed. The design of the proposed air coil sensor based on an inductive sensor is further investigated to improve its sensitivity. This paper investigates the results pertaining to the effects of the air coil structure of an oil palm fruit sensor, taking consideration of the used copper wire diameter ranging from 0.10 mm to 0.18 mm with 60 turns. The flat-type shape of air coil was used on twenty samples of fruitlets from two categories, namely ripe and unripe. Samples are tested with frequencies ranging from 20 Hz to 120 MHz. The sensitivity of the sensor between air to fruitlet samples increases as the coil diameter increases. As for the sensitivity differences between ripe and unripe samples, the 5 mm air coil length with the 0.12 mm coil diameter provides the highest percentage difference between samples and it is amongst the highest deviation value between samples. The result from this study is important to improve the sensitivity of the inductive oil palm fruit sensor mainly with regards to the design of the air coil structure. The efficiency of the sensor to determine the maturity of the oil palm FFB and the ripening process of the fruitlet could further be enhanced