Machine failure in modern industry leads to lost production and reduced competitiveness. Maintenance costs represent between 15% and 60% of the manufacturing cost of the final product, and in heavy industry, these costs can be as high as 50% of the total production cost. Predictive maintenance is an efficient technique to avoid unexpected maintenance stops during production in industry. Vibration measurement is the main non-invasive method for locating and predicting faults in rotating machine components. This paper reviews the techniques and tools used to collect and analyze vibration data, as well as the methods used to interpret and diagnose faults in rotating machinery. The main steps of this technique are discussed, including data acquisition, data transmission, signal processing, and fault detection. Predictive maintenance through vibration analysis is a key strategy for cost reduction and a mandatory application in modern industry.

Analog integrated circuits are present in most electronic systems. They must be designed carefully in order to achieve the required performance specifications. Although almost half of the design effort in a chip is spent in analog modules, there are not yet a consolidated industry of analog design automation tools capable to fully synthesize analog circuits in a fast and generic way. This is due the fact that automatic or semi-automatic design tools have to deal with high design complexity, which includes several specifications and design variables. A lot of research effort have been done in this field in the past years, proposing methods for automating parts of the design flow, from topology selection to devices sizing and layout generation. This paper presents a review of the state-of-the art in analog design automation methods, focusing on techniques and algorithms used to size robust circuits while efficiently exploring the design space.

The analog part of a mixed-signal integrated circuit represents a great amount of the circuit sizing effort. It is necessary to size each device separately and, in cases with several variables, the design space becomes quite large. The analog integrated circuit sizing can be modeled as an optimization problem and solved by optimization heuristics. In this work, we compare three bio-inspired heuristics to size a two-stage CMOS Miller operational transconductance amplifier: Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Firefly Algorithm (FA). The goal is to evaluate the applicability of these heuristics for the analog sizing problem and to determine the best configuration of the algorithms parameters for optimizing performance of the generated circuit, mainly power consumption and silicon area. Results show that PSO and CS are more suitable to find optimized solutions, while FA presents less efficient exploration of the design space. Although PSO is faster and generates good solutions, the best overall solution was achieved with CS algorithm.