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

Condition monitoring of helical gears using automated selection of features and sensors

The selection of most sensitive sensors and signal processing methods is essential process for the design of condition monitoring and intelligent fault diagnosis and prognostic systems. Normally, sensory data includes high level of noise and irrelevant or red undant information which makes the selection of the most sensitive sensor and signal processing method a difficult task. This paper introduces a new application of the Automated Sensor and Signal Processing Approach (ASPS), for the design of condition monitoring systems for developing an effective monitoring system for gearbox fault diagnosis. The approach is based on using Taguchi's orthogonal arrays, combined with automated selection of sensory characteristic features, to provide economically effective and optimal selection of sensors and signal processing methods with reduced experimental work. Multi-sensory signals such as acoustic emission, vibration, speed and torque are collected from the gearbox test rig under different health and operating conditions. Time and frequency domain signal processing methods are utilised to assess the suggested approach. The experiments investigate a single stage gearbox system with three level of damage in a helical gear to evaluate the proposed approach. Two different classification models are employed using neural networks to evaluate the methodology. The results have shown that the suggested approach can be applied to the design of condition monitoring systems of gearbox monitoring without the need for implementing pattern recognition tools during the design phase; where the pattern recognition can be implemented as part of decision making for diagnostics. The suggested system has a wide range of applications including industrial machinery as well as wind turbines for renewable energy applications