Was sind die Vor- und Nachteile des Einsatzes von Predictive Maintenance für automatisierte Systeme?

Termografialo TODO. Una de las mejores inversiones que se puede hacer para realizar el mantenimiento preventivo de un sistema de control es comprar una cámara termográfica. Las fotos térmicas dan una vista aproximada del final de la vida útil de un componente electrónico. Cuando este se acerca al final de su vida útil los componentes electrónicos empiezan a degradarse y a calentarse más. Esto suele pasar entre 6 meses a un año antes de que el equipo fallezca finalmente. Saber que tienes una tarjeta a más temperatura de lo habitual X meses antes de que pase algo, es una ventaja temporal que te permitirá verificar si tienes repuestos o necesitas comprarlos. Ten por seguro que si tienes un componente por encima de 100°C algo malo va a pasar.

✅ Predictive maintenance mitigates the risk of sudden breakdowns in older machines. ✅ Fixing issues early saves money by avoiding big and expensive repairs. ✅ By fixing only what needs fixing, you use your workers' time and spare parts more efficiently, saving money.

Pros of predictive maintenance for automated systems: Cost savings, increased efficiency, enhanced safety, data-driven insights, and sustainability benefits. Cons: Initial implementation costs, data complexity, false alarms, technical expertise requirements, and potential system integration challenges.

Predictive maintenance is very new for the automation industries, but very effective when it's trained with good quality of data. By using predictive techniques, not only we can avert unplanned failures but also increase the reliability of the automation system. But this technique is very new, you may encounter a high number of false triggers and it may result in no action on the valid alert. And the predictive modeling requires a good amount of initial budget with dedicated staff to support this deployment.

El mantenimiento predictivo ahorra costes por cuanto puede alargar los períodos de mantenimiento preventivo iniciales, mejorar indicadores de indisponibilidad, OEE y menor gasto en consumibles. Además, permite reducir las paradas no planificadas y dar avisos tempranos de futuras fallas a las que la organización pueda responder de manera planificada, desviar producción a otras plantas, preparar repuestos y personal para la intervención así como reducir tiempos de intervención.

One of the challenges is the old machines and technologies. ✅ Outdated technology may not support advanced maintenance tools, necessitating substantial upgrades. ✅ Upgrading older machines to meet the requirements of predictive maintenance may face resistance due to the perceived complexity and costs involved.

Uno de los desafíos más importantes es lograr la sinergia con para el cumplimiento de las metas entre herramientas+gente+procesos. Estos tres elementos son muy importantes sobre todo en la era de la transformación industrial. Herramientas son todas aquellas aplicaciones y/o soluciones que se puedan estar utilizando, la gente se refiere a la organización (entrenamiento, experiencia y personas) y procesos son los procedimientos y procesos internos que se tengan cuáles será el framework para el cumplimiento de las metas.

One of the challenges is to stick to planning. It is a race against time. Addressing potential problems (repairing, optimising, adjusting) BEFORE they become catastrophic and costly failures. It's getting down to the really IMPORTANT before the storm catches us unawares (the urgent).

El desafío principal es tener los datos para determinar el fallo. Empezando por el modo de fallo y siguiendo por la información adecuada y adecuadamente actualizada que permita el análisis. Esta información requiere de sensórica que no es parte del proyecto original y de plataformas para la gestión de la información que provean. Además, conocer el modelo de falla implica saber donde ubicar los sensores que puedan anticipar el fallo. Además, el tratamiento de datos requerirá en cada caso análisis en el dominio del tiempo (real, en media ,regresion) pero en otras veces será en espectro de frecuencias, distribuciones estadísticas, transformadas de Fourier, Laplace etc.

Challenges of Predictive Analysis: Data Quality: The quality of data is crucial for predictive analysis, and unreliable or incomplete data can lead to inaccurate predictions. Integration of Systems: Integrating various systems and sensors to collect and analyze data can be challenging, and different systems may use different data formats, making it difficult to integrate them. Expertise: Developing and implementing predictive maintenance systems requires specialized knowledge and expertise in data analysis and machine learning. Cost: Implementing a predictive maintenance system can be costly, requiring investments in hardware, software, and training.

Despite challenges, predictive maintenance can be a valuable strategy for automated systems when implemented properly. To maximize its benefits, assess the business and system's needs and goals, considering criticality, availability, performance, and cost of equipment. Choose tools and methods based on data availability, quality, and frequency, while considering failure modes, patterns, maintenance actions, and policies. Rather than deploying predictive maintenance system-wide at once, implement and test it incrementally. Constant monitoring and updating are necessary to adjust and optimize the system to changing conditions.

An initial idea would be to implement new technologies such as IIoT, Big Data, ML/AI, gradually or in phases, either by type of machinery (motors, ovens...) or by process (washing/sterilisation, digestion...), following a scheme of phases as follows: BASIC STAGE: Remote monitoring: Sensors and analysers for monitoring and preventive control. Predictive Analysis: Use of data and ML/AI to make appropriate decisions on when to schedule preventive activities. Implementation of Predictive Maintenance: Performing as recommended by algorithms and models.

Data Collection: The data should include historical readings and parameters that may be indicative of defects or failures. Data Preprocessing: This step involves data imputation, normalization, and transformation to make the data suitable for analysis. Feature Selection: Identify the most relevant features (readings and parameters). Data Splitting: Split the dataset into training and testing sets. Model Selection: Choose an appropriate predictive model for the task. Common models for predictive analysis include decision trees, random forests etc Model Training: Train the selected model using the training dataset. Model Evaluation: Evaluate the model's performance using the testing dataset.

To overcome the challenges, there are practical steps that can be taken to ensure success:- 1. Start with the basics- research, plan, and develop a strategy. 2. Make sure to stay up to date with current industry standards and best practices. 3. Set realistic goals and objectives. Establishing a clear plan of action can help you determine how to measure success and identify any potential roadblocks. 4. Stay vigilant and monitor the process. Regularly assess any changes in the process or technology and make sure to adjust your strategy accordingly. 5. Identify and address any potential risks. Take the necessary steps to anticipate and mitigate any potential risks.

Invest in Advanced Analytics: Utilize machine learning and AI algorithms to analyze data and predict equipment failures accurately. Cost-Benefit Analysis: Conduct a thorough cost-benefit analysis to justify investment in predictive maintenance technology. Training and Skill Development: Provide training and upskilling opportunities for personnel to enhance their data analysis and predictive maintenance capabilities.

Predictive maintenance is a tool which is now more meaningful because of the AI technology. However it is not always the ultimate solution. As it is mainly based on the data collected, if the data quality is not good or comprehensive data requirements are not met, it will be ineffective.

La conectividad inalámbrica, las plataformas de IoT y las capacidades de computación en Edge y nube permiten abordar proyectos de sensórica orientados a mantenimiento predictivo en unos rangos de plazos y costes que eran impensables apenas pocos años.

No sé si en la actualidad se usa pero yo optaría por hacer un histórico de como trabajan las redes principales de control (Ethernet - RS485 - FO) y ver la tasa de transmisión, pérdidas de señal, ganancias, etc. Para tener data que posteriormente se lleve a un modelo de machine learning (Con todo el boom que ha desatado en la actualidad con lo del 4.0) que pueda predecir el momento exacto en que se deba dar mantenimiento al hardware/Software de dicha red.

Maintenance of machinery is crucial to ensure the safe and efficient operation of vessels. However, traditional maintenance approaches, such as scheduled maintenance or reactive maintenance, can be time-consuming, costly, and may not address underlying issues. Predictive maintenance, which involves analyzing data to identify patterns and predict potential failures, is an alternative approach that can help prevent breakdowns and reduce maintenance costs.

Following on from my previous comment. After a basic stage, an advanced stage can follow to complement what has been obtained so far: Automation and Robotics: Incorporate autonomous systems that minimise human tasks efficiently and safely. Total Integration in the Value Chain: Connect preventive maintenance with other enterprise systems such as ERP, MES..... Continuous optimisation with AI: Close the loop with self-learning systems to improve predictions. Obviously all of this requires commitment from the ENTIRE organisation and strict adherence to the plan. As important as it is to establish a business, it must be maintained so that it will endure over time according to the Mission and Vision.