X-ray-based inspection systems are a well-accepted technique for identification and evaluation of internal defects in castings, such as cracks, porosities, and foreign inclusions. In this paper, some images showing typical internal defects in the castings derived from an X-ray inspection system are processed by some traditional methods and wavelet technique in order to facilitate automatic detection of these internal defects. An X-ray inspection system used to detect the internal defects of castings and the typical internal casting defects is first addressed. Second, the second-order derivative and morphology operations, the row-by-row adaptive thresholding, and the two-dimensional (2-D) wavelet transform methods are described as potentially useful processing techniques. The first method can effectively detect air-holes and foreign-inclusion defects, and the second one can be suitable for detecting shrinkage cavities. Wavelet techniques, however, can effectively detect the three typical defects with a selected wavelet base and multiresolution levels. Results indicate that 2-D wavelet transform is a powerful method to analyze images derived from X-ray inspection for automatically detecting typical internal defects in the casting.

The analysis of pre-epileptic seizure through EEG (electroencephalography) is an important issue for epilepsy diagnosis. Currently, there exist some methods derived from the dynamics to analyse the pre-epileptic EEG data. It is still necessary to create a novel method to better fit and explain the EEG data for making sense of the seizures’ predictability. In this paper, a fractal wavelet-based spectral method is proposed and applied to analyse EEG recordings from rat experiments. Three types of patterns are found from the 12 experiments; moreover three typical cases corresponding to the three types of seizures are sorted out and analysed in detail by using the new method. The results indicate that this method can reveal the characteristic signs of an approaching seizure, which includes the emergence of long-range correlation, the decrease of anti-persistence behaviour with time and the decrease of the fractal dimension. The pre-seizure features and their implications are further discussed in the framework of the theory of criticality. We suggest that an epileptic seizure could be considered as a generalized kind of ‘critical phenomenon’, culminating in a large event that is analogous to a kind of ‘critical point’. We also emphasize that epileptic event emergence is a non-repetitive process, so the critical interpretation meets a certain number of cases.

In recent years, a number of machining centers have been built using linear motors. These machining centers have great potential for precision and high-speed machining. Nevertheless, a number of problems remain unsolved, such as monitoring and control. This paper presents a new tool breakage monitoring method for this type of machining center using the current signal of the linear motor. First, the relationship between the cutting force and the motor current is analyzed. Then, the new tool breakage method is presented. From a mathematical point of view, the new method uses a nonlinear energy operator to capture the abrupt changes of the motor current signal, which is directly related to the tool breakage. The experiment validation is included.

Most practical industrial process data contain contributions at multiple scales in time and frequency. Unfortunately, conventional statistical process control approaches often detect events at only one scale. This paper addresses a new method, called multiscale statistical process monitoring, for tool condition monitoring in a machining process, which integrates discrete wavelet transform (WT) and statistical process control. Firstly, discrete WT is applied to decompose the collected data from the manufacturing system into uncorrelated components. Next, the detection limits are formed for each decomposed component by using Shewhart control charts. A case study, i.e., tool condition monitoring in turning using an acoustic emission signal, demonstrates that the new method is able to detect abnormal events (serious tool wear or breakage) in the machining process.

This paper presents a new condition monitoring method based on a latent process model. The method consists of three steps. First, a sensor signal is modeled by a latent process model that is a combination of a time-varying auto-regression model and a dynamic linear model, which decomposes the signal into several components, and each component represents a different part of the monitored system with different time-frequency behavior. Based on the latent process model, important features are extracted. Finally, using the generative topographic mapping, the selected features are mapped to a lower (two)- dimension space for classification. The proposed method is tested in condition monitoring of sheet metal stamping processes. A large number of experiments were conducted. In particular, two cases are presented in detail. From the testing results, it is found that the proposed method is able to detect various defects with a success rate around 98%. This result is significantly better than the conventional artificial neural network method. In addition, the new method is a self-organizing method and hence, little training is necessary. These advantages make the method very attractive for practical applications.