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.

Oil palm ripeness’ main evaluation procedure is traditionally accomplished by human vision. However, the dependency on human evaluators to grade the ripeness of oil palm fresh fruit bunches (FFBs) by traditional means could lead to inaccuracy that can cause a reduction in oil palm fruit oil extraction rate (OER). This paper emphasizes the fruit battery method to distinguish oil palm fruit FFB ripeness stages by determining the value of load resistance voltage and its moisture content resolution. In addition, computer vision using a color feature is tested on the same samples to compare the accuracy score using support vector machine (SVM). The accuracy score results of the fruit battery, computer vision, and a combination of both methods’ accuracy scores are evaluated and compared. When the ripe and unripe samples were tested for load resistance voltage ranging from 10 Ω to 10 kΩ, three resistance values were shortlisted and tested for moisture content resolution evaluation. A 1 kΩ load resistance showed the best moisture content resolution, and the results were used for accuracy score evaluation comparison with computer vision. From the results obtained, the accuracy scores for the combination method are the highest, followed by the fruit battery and computer vision methods.

High-efficiency motors are being gradually introduced in many industrial applications because of their positive impacts on the environment by reducing energy consumption and CO₂ emission. In this respect, line start permanent magnet synchronous motors (LS-PMSMs) have been introduced recently. Due to their unique configuration, LS-PMSMs allow the obtaining of super premium efficiency levels, accompanied with a high torque and power factor. However, since the use of LS-PMSMs in the industry is in its infancy, no efficient scheme has yet been reported for broken rotor bar (BRB) fault detection in this type of motor. Accordingly, the main aim of this research is to investigate the fault-related feature for BRB faults on LS-PMSMs. In this regard, a simulation model and experimental setup for the investigation of BRB in LS-PMSM are implemented. The detection strategy for BRB in LS-PMSM proposed here is based on the monitoring of the start-up current signal and discrete wavelet transform. The entropy features are used as fault-related features for BRB faults. Finally, the ability of these features is validated for the detection of BRB in LS-PMSM through statistical analysis. In this research, the importance of the starting load is also considered for BRB detection in LS-PMSMs.

This paper aims to study a triple flat-type air coil inductive sensor that can identify two maturity stages of oil palm fruits, ripe and unripe, based on the resonance frequency and fruitlet capacitance changes. There are two types of triple structure that have been tested, namely Triple I and II. Triple I is a triple series coil with a fixed number of turns (n = 200) with different length, and Triple II is a coil with fixed length (l = 5 mm) and a different number of turns. The peak comparison between Triple I and II is using the coefficient of variation $c_v$, which is defined as the ratio of the standard deviation to the mean to express the precision and repeatability of data. As the fruit ripens, the resonance frequency peaks from an inductance–frequency curve and shifts closer to the peak curve of the air, and the fruitlet capacitance decreases. The coefficient of the variation of the inductive oil palm fruit sensor shows that Triple I is smaller and more consistent in comparison with Triple II, for both resonance frequency and fruitlet capacitance. The development of this sensor proves the capability of an inductive element such as a coil, to be used as a sensor so as to determine the ripeness of the oil palm fresh fruit bunch sample.

There are many factors affecting oil extraction rate (OER) but a large contributor to high national OER is by processing good-quality fresh fruit bunches (FFB) at the mills. The current practice for grading oil palm fruit bunches in mills is using human graders for visual inspection, which can lead to repeated mistakes, inconsistent evaluation results, and many other related losses. This study aims to develop a fruit maturity sensor that can detect oil palm fruit maturity grade and send indication to the user whether to accept or reject the bunches. This study focuses on fruit battery principle and applying the charging concept to the fruit battery in order to generate significant load voltage readings of oil palm fruit battery. The charging process resulted in amplified load voltage readings, which were 4 times more sensitive to changes as compared to normal fruit battery without charging process. From the load voltage readings, the fruits can be characterized into their maturity grade based on moisture content. It was determined that fruits with moisture content less than 44% and average load voltage, V_avg, between 20 to 30 mV are considered ripe fruits.

Magnetic gears (MGs) technology is studied widely among research institutions, with several improvements being documented. This development attracts a high amount of attention due to the demand in the development of magnetic gears towards higher performance than the conventional mechanical counterpart. In general, the design is complicated and there is a lack in detailed references for designing an MG for specific transmission torque as required by its application. Trial-and-error approaches have been the norm in achieving the desired torque by referring the existing MGs for the desired value of torque. This paper presents a new simplified approach towards designing an MG for the required torque and size by referring through a Maximum Transmission Torque Line (MTTL) reference. Finite element method (FEM) is used in analyzing randomly designed magnetic gears with various parameters towards the desired values of the MTTL. The proposed approach of MTTL is a new approach to estimate the total volume of permanent magnets (PMs) required for the MG to achieve the desired transmission torque. The reference line is used to generate equation relating the specific parameters of MG to develop the simplified design of MG based on the estimated total volume of PMs. This simplified way details to 8.5% of error in targeting the desired transmission torque, a means and way for the first stage of the MG design approach to reduce the conventional approaches.