# Intelligent Framework Design for Quality Control in Industry 4.0
## 1 Introduction
By analyzing vibration data, researchers and practitioners can identify abnormal operating conditions, detect incipient faults, and predict the remaining useful life (RUL) of gears. Vibration data are collected from motors and other industrial equipment with vibration sensors installed on the surface of this rotatory equipment. The vibration patterns are transferred to the sensor via the shaft of motors [13]. Vibration sensors include accelerometers and laser sensors. Microphones can also be used to sense and collect sound energy generated during the operation of different gear systems of machines. Accelerometers are widely used due to their availability and they are less expensive [14]. Lasers provide highly accurate vibration patterns; however, they are expensive and they cannot be installed on hot surfaces of industrial equipment [15]. Microphones are also not preferred in industrial environments because they can collect background noise along with the sound of the machine [16]. Using a microphone would require speech-processing techniques to separate the background noise from the sound patterns of the machines [17]. Figure 1 provides the vibration pattern obtained from the accelerometer for eccentricity fault under an 80 Nm load. Machine vision-based systems use industrial automation with image processing. Quality control has been one of the core areas of the application of machine vision in industry. Machine vision, along with machine learning, provides economical, reliable, and fast inspection that enhances quality as well as productivity [7]. Developing a machine vision model is a challenging job as each application is unique and has its own needs and desired outcomes. The machine vision model combines the use of lighting, optics, and digital cameras, coupled with machine learning and image processing applications. The applications  
Figure 1: Accelerometer data for eccentricity fault at 80 Nm load. in the developed machine vision model process the images and decide actions based on processed data. The machine vision system uses advanced hardware including the optics, cameras, and carefully selected lighting systems to collect the correct image data under different challenging conditions. In certain cases, if the collected images do not have a better contrast in white light, a series of LED banks can be used through red, blue, and green lights to capture acceptable images. The frequency and intensity of the lighting system can also be changed to obtain workable images [18]. An illustration of a machine vision system is shown in Figure 2. Once the workable image data are collected, each pixel of the image is assigned an integer value ranging from 0 to 255. In grayscale images, a completely white pixel is assigned a value of 255, and a completely dark pixel in the image is assigned a numeric value of zero. After the image processing software has assigned all the pixels with the numerical values in the image, the software system checks for patterns, variations, and edges and then combines them to detect shapes. The underlying software also enables the machine vision system to detect the objects and identify the sizes and shapes of the objects in the image. The software identifies the presence of faults and their precise location on a product and whether the faults are a reasonable size to pass the quality inspection. The image preprocessing process is carried out on raw images obtained from cameras. These raw images have many problems like noise, improper edges, and blurred focus, and sometimes, the image quality is disturbed by lighting sources, causing shadows to appear in the images. Preprocessing is carried out to obtain images free of the above-mentioned problems. The preprocessing methods may include scaling, contrasting, edge detection, histogram generation, image segmentation, etc. [20]. The rest of the paper is organized as follows: Related works have been provided in Section 2. Section 3 provides details about the methodology, data insights, and data processing techniques.