## 1 Introduction  
Industry 4.0 concepts lead to the generation of large amounts of data, many of which are available in real time to end users; however, these data are often not effectively used to their maximum potential for prediction, diagnosis and prescription for decision-making related to the production process and maintenance of industrial equipment.  
The use of process variables and sensors can assist in the early diagnosis of common failure modes that negatively impact product quality in industries and the availability of critical equipment. Digitalization concepts, the application of advanced statistical concepts and data science can enable the early diagnosis of chronic failure modes, reduce machine downtime, reduce risk and improve resource allocation. Well-established data analysis methodologies such as CRISP-DM establish a framework for analyzing complex business problems in a structured way.  
Equipment and component failure data can be analyzed in statistical reliability analysis models to estimate the probability of failure over time, and sensor data can indicate different failure states, enabling the early assessment of a component's end of life through sensor reading pattern classification. Areas of knowledge seeing growing interest, such as online predictive maintenance and RUL--remaining useful life--aim to establish the relationship between sensor readings and the remaining useful life of machines and components. Previous studies employed similar methodologies in the analysis of sensors to identify failures in bearings, gears, wind turbines, marine turbines, aeronautical engines, filters, electric batteries and other equipment in the early prediction of failures through the identification of patterns and anomalies in equipment process variables in the time or frequency domain, but most of these studies considered the use of sensors dedicated specifically to this purpose.  
The objective of this work is to present a methodology that allows the processing of large volumes of data, enabling the prediction of the reliability and remaining useful life of industrial systems and components from data that are already collected and processed in automation systems such as PIMSs--Plant Information Management Systems--and the MES--Manufacturing Enterprise System--without the need for additional sensors. Sensor readings used for simple problem identification in industrial automation in high-low alarms and high-high and low-low safety protocols can be used in analytical models to identify more complex process anomalies earlier.  
This work presents a case study for ceramic plates from vacuum filters in an iron ore slurry filtration industry where operational process variables and failure data were used to determine a model for their remaining useful life. Ceramic plates represent one of the highest costs of an industrial filtration plant, corresponding to more than 40% of its annual operational budget, and they also have a high impact on filter productivity and iron ore moisture content. These characteristics lead to ceramic plate components that must be replaced as minimally as possible, keeping their performance reliably within process requirements. Replacing them earlier than required causes unnecessary costs, whereas late replacement can cause a loss of process control.