Remote data acquisition for condition monitoring of wind turbines

While the number of offshore wind turbines is growing and turbines getting bigger and more expensive, the need for good condition monitoring systems is rising. From the research it is clear that failures of the gearbox, and in particular the gearwheels and bearings of the gearbox, have been responsible for the most downtime of a wind turbine. Gearwheels and bearings are being simulated in a multi-sensor environment to observe the wear on the surface

Using SCADA data for wind turbine condition monitoring - a review

The ever increasing size of wind turbines and the move to build them offshore have accelerated the need for optimised maintenance strategies in order to reduce operating costs. Predictive maintenance requires detailed information on the condition of turbines. Due to the high costs of dedicated condition monitoring systems based on mainly vibration measurements, the use of data from the turbine Supervisory Control And Data Acquisition (SCADA) system is appealing. This review discusses recent research using SCADA data for failure detection and condition monitoring, focussing on approaches which have already proved their ability to detect anomalies in data from real turbines. Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems. Potential for future research on the use of SCADA data for advanced turbine condition monitoring is discussed

Fuzzy Logic Application, Control and Monitoring of Critical Machine Parameters in a Processing Company

The processing company under study found out that the boiler was the key machine and needs artificial intelligence monitoring and control. It was simulated under Matlab software and oil level, and pressure and temperature were to be modelled and controlled using the programmable logic controller (PLC) with a fuzzy logic controller as the main brain of control. The company is for processing of fruits to produce juice

Design of a fuzzy logic control system for monitoring gearbox jamming in a bottle washer machine

The purpose of this research was to come up with an intelligent monitoring tool to reduce the number of breakdowns of a beverage company bottle washer. The Fuzzy Logic system was derived among other artificial intelligent systems to be best appropriate to solve the breakdown challenges of the bottle washer. A gearbox is always jamming and it is not easy to troubleshoot the breakdown cause and fuzzy logic is a tool that was used for monitoring. The researchers carried out a company audit, interviews and administered questionnaires in order to gather relevant data. The results were used in intelligent condition-based-maintenance modelling to solve the problem using fuzzy logic system and it was found that oil level should be always above 40% otherwise the gearbox will be made to stop. Torque is supposed to have a range of values accepted from 0-8 000Nm beyond that we consider the stoppage of the gearbox. Very higher torques above 10000Nm damages the machinery

Computational intelligence for fault diagnosis in gearbox systems

Employing an efficient condition monitoring system in industrial applications is an important factor in improving the quality of production and increasing the operational life of machines by revealing machine faults at the earlier stage. Damage in gearbox system is one of the most catastrophic failures in machineries. Any defects related to a gearbox will in influence the performance of an entire mechanical system. A reliable and efficient fault diagnosis system is required to reduce the maintenance cost and downtime, thereby preventing machinery performance degradation and failure. Many condition monitoring and fault diagnosis systems are investigated in the literature for gearbox fault detection and diagnosis. However, there are still many challenges to tackle mainly due to the complex nature of gearbox structure, limited access to the component to be monitored and the low signal-tonoise ratio experienced especially when operating machineries under fault conditions. The aim of this research is to develop a systematic methodology for the design of condition monitoring systems for gearbox faults by investigating sensor selection, sensor location, and sensory features to be able to diagnose a fault accurately. Therefore, the goal is to select reliable techniques at each stage in order to improve the reliability of the fault diagnosis system. Different sets of experiments based on gearbox conditions are conducted using several sensors including vibration, acoustic emission, speed, and torque. Measured signals are analysed using conventional and advanced signal processing and data analysis methods including time, frequency and time/frequency domains such as Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), and Wavelet analysis (WT). Several statistical and mathematical techniques have been proposed as features extraction methods to reduce the dimensionality and select appropriate information. For this research, a single stage gearbox system with two main type of faults (pitting and broken teeth) with various degrees of damage in helical gear are used to evaluate the proposed approach. This research investigated the relationship between sensor location and detecting the fault in gearbox system. A methodology has been proposed for locating indirect monitoring sensors such as acoustic emission and vibration on gearbox to obtain high quality information regarding the behaviour of machine condition. The methodology is designed to evaluate the optimum sensor positioning for detecting faults in the gearbox system. A novel gearbox monitoring approach named an Automated Sensor and Signal Processing Selection for Gearbox system (ASPSG) has been applied to select the most reliable and sensitive sensors, features and signal processing methods based on optimal sensor location. The ASPSG approach is based on simplifying complex sensory signals into a group of Sensory Characteristic Features (SCFs) and evaluating the sensitivity of these SCFs in detecting gearbox faults. The aim of this approach is to enhance the performance of monitoring system of gearbox fault detection and to reduce the number of sensors required in the overall system and reduce the cost. To implement the suggested ASPSG approach two strategies are proposed: automated system based on Taguchi's orthogonal arrays and stepwise system using (Linear Regression (LR), Fuzzy Rule Based System (FRBS) and Principal Component Analysis (PCA), techniques ). To evaluate both strategies, four different classification models are employed using supervised and unsupervised neural networks. Both strategies have been implemented to prove the capability of the suggested approach. A cost reduction is performed based on removing the least utilised sensors without losing the performance of the condition monitoring system. The results show that the ASPSG approach can provide a systematic design methodology for condition monitoring systems for gearboxes and that it is capable of detecting faults in a gearbox system with less cost and reduced number of experiments. Consequently, the findings of this research prove that the sensor location could have significant effect on the design of the condition monitoring system and its performance

Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines

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