Osmotic dehydration kinetics using parameters moisture loss and solid gain during osmotic dehydration of carrot strips were studied. Carrot strips of 30×10×2 mm size were osmotically dehydrated in sugar syrup of 40°B, 50°B and 60°B. During osmotic dehydration process syrup solution to carrot sample ratio was kept 6:1. The process was carried out at various temperatures 27°C,40°C and 50°C for time period of 15, 30, 60, 90, 120 and 180 minutes. Mass transfer kinetics of the two main parameters of process, moisture loss and solid gain data were studied using five different mathematical models. The models used include Azuara, Peleg, Magee, Power and Penetration Model. Amongst the applied model Azuara’s and Peleg model were found to be best fitted for moisture loss while for solid gain Power law and Magee’s model were found to be best fit.

In the majority of machines, bearings are among the most crucial components. Bearings are so important that they have been the subject of intensive research and ongoing development throughout the years. Often, bearing fails to reach its expected service life, resulting in failures that cause economic losses. Therefore, there has been a growing interest in research on bearing failure diagnosis systems due to the availability of condition monitoring techniques. Fault feature extraction techniques with the application of signal processing methods and machine learning techniques introduce an Intelligent Fault Diagnosis system that can identify and diagnose the bearing faults. Many researchers have been interested in such techniques in recent decades, which use artificial intelligence to diagnose machine health conditions. In this article, the authors have explored certain fault diagnosis methodologies based on signal processing and machine learning. From the discussed literature review, a research gap for future work has been defined.