Groundwater exploitation in Awka (Anambra – Nigeria) and environs: Prospects, and challenges while drilling and its mitigation measures

Groundwater exploitation (borehole drilling) was carried out around Awka and environs in Anambra State, Southeastern Nigeria, to understand the underlying rock units encountered while drilling, differentiate boreholes with confined aquifers from those with unconfined aquifers, delineate the probable aquiferous zones from the borehole data, evaluate the challenges encountered while drilling (both geologic and technical), and identify mitigation measures employed to address these challenges. Detailed geologic log information of the boreholes was produced to illustrate the rock units encountered while drilling. Four rock units were identified, namely: shale, sandstone, clay, and gravel. These rock units were exposed within the Imo Formation and the Nanka Formation that underlie the study area. Results from the geologic log information of the boreholes indicate that the water table within the study area ranges from 11.2 m to 56.5 m from the soil surface, and the probable aquiferous zones vary from 6.8 m to 23.3 m in thickness. A detailed look at the lithologic logs of the boreholes show that 50% of the drilled boreholes possess confined aquifers while the remaining 50% have unconfined aquifers. A careful appraisal of the challenges encountered, which are mainly geologic, is strictly attributed to the geologic formation of the study area. Other technical challenges have been derived from mechanical faults developed during drilling.

A hybrid fault diagnosis scheme for railway point machines by motor current signal analysis

Proper analysis of point machine current signal provides pervasive information of health status of their internal components. Point machines are subjected to several failure modes during their operation. “Gearbox,” “ball bearing,” “lead screw,” and “sliding chair” faults are among common mechanical failure modes. In this article, a two-stage prediction innovative process is proposed using Fault Detection based Decision Tree strategy (FDDT) where the healthy and faulty modes are first determined, followed by classifying the types of mechanical faults based on Parallel Neural Network Architecture and Fuzzy System (PNNFS). To differentiate between faulty and healthy point machines, some relevant features are extracted from the motors’ current signals which are used as input data for the proposed FDDT_PNNFS method. Feature selection has been performed using the ReliefF to select the dominant predictors in the point machine. Firstly, the Decision Tree (DT) algorithm is used to obtain a classifier model based on the offline training method for fault detection. The performance of DT is compared with the support vector machine algorithm. In the second stage, faulty data is fed to a bank of Neural Networks, designed in Parallel Neural Network Architecture (PNNA), which is used for identifying the type of failures. Each Neural Network Algorithm (NNA) is responsible for detecting only one type of failure and assessment of the NNA outputs shows the final failure of the point machine. If there is a discrepancy between the outputs of the NNAs, fuzzy logic plays the role of modifier and judges among outputs of NNAs and determines the more probable fault type.

Diagnosis of Mechanical Faults Affecting a Hydroelectric Group by Vibration Analysis

This article deals with a diagnostic approach based on a predictive / conditional maintenance approach of a hydroelectric group. The technique used is based on the spectral analysis of the vibration signals, as well as on the orbital analysis of the bearings displacements. To do this, test protocols in different operating regimes are carried out, based on the collection of data measured according to the multisensor approach, the aim of which is to identify the predominant faults. The positions of the sensors are placed as close as possible to the bearings on the rigid structure of the hydroelectric group in accordance with the recommendations of standard ISO 10816-5. The evaluation approach is based on the analysis of the amplitudes of the vibration speeds, the aim of which is to identify the type of faults, as well as on the bearing displacement indicators in order to classify them in the pre-established zones according to thresholds recommended by the Standard. Therefore, a recommended tuning intervention can be planned in order to restore the unit to its proper operating condition, the aim of which is to increase its service life and improve fuel efficiency. Keywords—Diagnostic, Vibration analysis, Hydroelectric group, Multi-sensor approach, Standard

Influence of Noise on Fault Diagnosis of Transformer based on Vibration Signal

Monitoring transformer vibration signals is a universal application method to realize the diagnosis of internal mechanical faults of transformers. However, the actual transformer operating is interfered by the noise of the surrounding electrical equipment, which reduces the accuracy of the vibration signal identification. This paper simulate the typical noise sources in the actual transformer operating environment, including fan noise and surrounding equipment fault noise, and explore the impact of different noise sources on the transformer vibration signal.

Induction Motor Bearing Faults Diagnosis Using Stator Current and Vibration Analysis

Several researches claim that the vibration technique, widely used in industry, is more efficient compared to the stator current analysis in the diagnosis of mechanical faults. On the other hand, researches show that the current technique is more advantageous especially in the diagnosis of electrical faults, in addition to the simplicity of the sensor positioning. The aim of this paper is to show that both diagnosis techniques can be complementary. For this, a comparative analysis of both diagnosis techniques performances is achieved. To this end, fault diagnosis of rolling element bearings used in induction motors is taken as an example, given the importance of bearings in energy transfer. Experimental results obtained show the complementarity of both techniques and their performances according to the faulty element of bearings.

A New Universal Domain Adaptive Method for Diagnosing Unknown Bearing Faults

The domain adaptation problem in transfer learning has received extensive attention in recent years. The existing transfer model for solving domain alignment always assumes that the label space is completely shared between domains. However, this assumption is untrue in the actual industry and limits the application scope of the transfer model. Therefore, a universal domain method is proposed, which not only effectively reduces the problem of network failure caused by unknown fault types in the target domain but also breaks the premise of sharing the label space. The proposed framework takes into account the discrepancy of the fault features shown by different fault types and forms the feature center for fault diagnosis by extracting the features of samples of each fault type. Three optimization functions are added to solve the negative transfer problem when the model solves samples of unknown fault types. This study verifies the performance advantages of the framework for variable speed through experiments of multiple datasets. It can be seen from the experimental results that the proposed method has better fault diagnosis performance than related transfer methods for solving unknown mechanical faults.

Measurement and analysis of vibrations of electric motors on board container ships

The purpose of this paper is to measure and analysis the vibrations of electric motors on board container ships, in order to reduce the maintenance costs and implicitly in making the optimal decisions. In general, the faults that underlie electric motors are primarily due to mechanical and electrical efforts. Mechanical stresses occur as a result the overloads and rapid load variations. On the other hand, the overcurrents and overvoltages are usually in the close accordance with the power supplies. In this regard the mechanical faults cannot be analyzed by changing the parameters like voltage, current, power, frequency but in practice we can do analysis by used the high-performance testers with intelligent software for measuring the motor vibrations. The MarVib DC650 tester was used in this paper for analysis and measurement the vibrations of electric motors.