Fault, which is the automated fault detection and diagnosis technology we often call, has made great use in industrial production and equipment maintenance. Many people are confused about what is going on and what practical problems it can solve. Next, let’s talk about practical things in this area.
Fault Basic concepts and core values
Fault In general, it is to use computer technology, sensor technology, and data analysis algorithms to combine things like computer technology, sensor technology, and data analysis algorithms to collect and analyze various parameters and signals during the operation of the equipment or system in real time, and then automatically identify whether there is a fault, where the fault is located, and what is the technical means caused! To put it simply, it means that the machine can discover and judge problems by itself without having to look at it bit by bit. This has a particularly good effect on improving equipment reliability, reducing and reducing maintenance costs, and provides global procurement services for weak current intelligent products!
Fault Key modules and implementation steps
1. Data acquisition module: This module mainly relies on various sensors, such as temperature sensors, vibration sensors, current and voltage sensors, which collect all kinds of physical quantities during the operation of the equipment and convert them into digital signals that computers can understand. The frequency and accuracy of the acquisition are particularly critical in this link, and you can't be careless!
2. Data preprocessing module: The collected data is often messy, with noise and missing values, so it has to be processed using filtering, smoothing, and interpolation methods to make the data clean and regular, laying a solid foundation for subsequent analysis.
3. Feature extraction module: Extract key information that can reflect the operating status of the equipment and fault characteristics from the processed data, such as the average, maximum, and minimum values in the time domain features, and the spectrum peaks in the frequency domain features, which will directly affect the accuracy of fault detection.
4. Fault algorithm module: There are many commonly used algorithms, such as rules-based algorithms, which turn expert experience into rules to match; there are also model-based algorithms that use mathematical models to simulate normal states for comparison; machine learning algorithms, such as neural networks and support vector machines, use a large amount of data to identify faults. Different algorithms are suitable for different scenarios, and they have to be selected and used according to the actual situation!
5. Diagnosis and early warning module: Once a fault sign is detected or a fault has occurred, the system will automatically analyze the type, location, and severity of the fault, and then issue an early warning signal to remind the maintenance personnel to take measures quickly and not let small problems become big troubles!
Fault Practical Tips and Questions
Q: What are the requirements for fault when selecting sensors
Answer: When choosing a sensor, you must first consider whether the type and range of measurement are suitable. For example, when measuring temperature, choose a temperature sensor, and when measuring vibration, choose a vibration sensor; then the accuracy and sensitivity must also meet the requirements, otherwise it will be troublesome if the measurement is not accurate. You must also consider the installation environment of the sensor. For example, for high-temperature, high humidity, and corrosive environments, you must choose a sensor with the corresponding protection level. In addition, the compatibility between the sensor and the data acquisition system is also very important. Don’t buy it and can’t be used!
Q: What should I do if the system false alarm rate is too high
Answer: The high false positive rate is really a headache. It can be solved from these aspects: First, optimize data preprocessing, filter the noise cleaner, and eliminate those abnormally jumping data points; Second, adjust algorithm parameters, such as setting the threshold more reasonably, or using more advanced machine learning algorithms to train the model with more and better data; Third, dynamic adjustments are made based on the actual operating conditions of the equipment. After all, the range of normal states is also different under different loads and different environments, and we cannot use rigid standards to judge in a constant manner!
My personal opinion is that fault is an inevitable trend in the development of industrial intelligence and can help enterprises greatly improve production efficiency and management level. However, when implementing it, you must choose appropriate software and hardware solutions based on your own actual situation, and pay attention to data quality and personnel training, so that it can truly bring out its value!
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