A brief discussion on predictive maintenance of Siemens instruments (translated)

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1. Introduction Today's process industry has increasingly higher requirements for on-site reliability. Instruments and equipment are generally required to operate continuously and reliably 24/7. Sudden faults and shutdowns will cause huge losses. Therefore, higher requirements are also put forward for equipment maintenance to avoid production losses and material waste to the greatest extent. According to statistics, in 1981, U.S. factories spent more than $600 billion on maintaining their critical equipment systems. In 1991, this cost had risen to more than 800 billion US dollars, and in 2000 it reached a record high of 1.2 trillion US dollars. These figures indicate that between one-third and one-half of these costs are wasted due to ineffective maintenance management methods. Although there are no statistics in this area in China, I believe that China's current situation in this area is similar to that of the United States at that time. Choosing wrong and primitive maintenance methods and backward maintenance concepts are the main reasons for this ineffective use of maintenance expenditures. Today, with the emphasis on economic benefits, more and more users are beginning to realize that the past method of simply relying on human intuition and experience to implement equipment maintenance plans can no longer meet the production needs of the process industry. 2. Equipment maintenance modes There are generally three modes of equipment maintenance.: Post-event maintenance, preventive maintenance and predictive maintenance. Post-event maintenance is also called "remedial maintenance", "post-failure maintenance", and "run-to-failure management". As the name suggests, it means repairing the equipment when it fails. This is a "no fault, no repair" maintenance method. It is also the most basic and simple maintenance method currently used by users in China. A plant that uses run-to-failure management spends nothing on maintenance until a machine or system fails. Run-to-failure is a reactive management technique that waits for a machine or equipment to fail before taking any maintenance action. Rather, this is a "no maintenance" management approach. It is also the most expensive method of maintenance management, with analysis showing that the cost of repairs performed in a reactive or after-the-fact maintenance mode is three times higher than the cost of the same maintenance performed in a planned or preventive maintenance mode. Scheduling repairs allows factories to minimize repair time and associated labor costs. It also provides a way to reduce negative impacts such as rapid delivery and production drops. The main costs associated with this maintenance management are: Ø High spare parts inventory costs ； Ø High overtime labor costs ； Ø Long machine downtime ； Ø Low production capacity. Preventive maintenance (Preventive Maintenance) and predictive maintenance (Predictive Maintenance) both belong to advance maintenance. Generally speaking, preventive maintenance can be divided into three types:: Ø Time-based maintenance plan Ø Performance-based maintenance plan Ø Condition-based maintenance plan Figure 1: Mean Time Between Failure (MTBF) Curve Time-Based Maintenance Planning: Maintenance with a time-based maintenance schedule is performed in specific cycles, for example, every two months or every six months. Performance-based maintenance plan: This can be done based on readings from counters installed at measuring points at individual equipment and functional locations. Some devices operate based on many parameters or states. These parameters or status information may be provided by the measuring points. The system performs maintenance when the counter on the equipment reaches a certain reading, for example, every 100 hours of operation, every 500 casting cycles, etc. Condition-based maintenance plan: Some equipment will be controlled by real-time status or operating parameters such as temperature. These parameters are real-time readings and need to be monitored in real time. The common hallmark of all these preventive maintenance programs is that they all have scheduling guidelines. All preventive maintenance management programs assume that equipment condition will deteriorate within the statistical timeframe typically applicable to that particular type of machine. For example, a certain kind of pump equipment usually needs to replace its worn parts after 12 months of operation. Using preventive maintenance techniques, the pump must be taken out of service and rebuilt after 11 months of operation. The problem with this approach is that operating modes and system- or device-related variables directly affect the normal operating life of the machine. The mean time between failures (MTBF) is different for pumps used to convey water and abrasive slurries (Figure 1). If the pump may not need maintenance after 11 months and still be functioning properly, the labor and materials spent on repairs by the user have been wasted. The second option using preventive maintenance is even more expensive. At the same time, there are also a lot of facts showing that frequent disassembly and shutdown for maintenance will lead to reduced output, increased maintenance costs, and also * * Reduces the actual service life of the equipment. In fact, experts suggest that one of the biggest causes of many industrial equipment failures is: Without doing rigorous analysis, we just go up and operate and repair the industrial equipment. Predictive maintenance (PM) is also called predictive maintenance or strategic maintenance. Predictive maintenance integrates equipment condition monitoring, fault diagnosis, fault (state) prediction, maintenance decision support and maintenance activities, and is an advanced maintenance method that has emerged in recent years. Predictive maintenance is actually a health-driven preventive maintenance program. Predictive maintenance does not rely on average life statistics (i.e., mean time between failures) within an industry or factory to plan maintenance activities. Instead, it directly monitors operating conditions, efficiency, heat distribution, and other indicators to determine the actual mean time between failures or efficiency losses that will jeopardize the operation of all critical system devices in the factory or facility. It has obvious advantages over preventive maintenance: Ø Overcome the blindness of preventive maintenance and be highly targeted. Different processing methods are adopted according to different conditions to reduce operation and maintenance costs. Ø Reduce outage (total maintenance) time, improve equipment reliability and availability coefficient, extend equipment service life, and better implement the policy of "safety first, prevention first". Ø Reduce maintenance workload and labor intensity, which is conducive to staff reduction and efficiency improvement, and improves economic benefits. 2. The Current Situation of Predictive Maintenance In the past few years, with the rapid development of instrumentation based on microprocessors or computers, as well as the continuous improvement of maintenance management systems, especially the rapid development and popularization of advanced communication technologies such as fieldbus and industrial Ethernet, major domestic and foreign manufacturers have launched various advanced predictive maintenance technologies. Dr. FitCh from the United States created the concept and theory of proactive maintenance based on predictive maintenance. The implementation of total quality management (TQM) and ISO 9000 has also doubled the demand for PDM plans. Both TQM and lSO 9000 require maintenance planning procedures to ensure that the process and all components can continue to perform at high performance. Predictive maintenance has a narrow sense and a broad sense in terms of the connotation and extension of the concept. Predictive maintenance in a narrow sense is based on "condition monitoring", emphasizing that "fault diagnosis" refers to irregular or continuous condition monitoring of equipment, and based on the results. Find out whether the equipment has abnormal status or failure trends, and then arrange maintenance in a timely manner ； Generalized predictive maintenance integrates condition monitoring, fault diagnosis, condition prediction and maintenance decision-making. Condition monitoring and fault diagnosis are the basis, condition prediction is the focus, and maintenance decisions lead to the final maintenance activity requirements. It is a systematic process. It brings maintenance management into the scope of predictive maintenance. Think through the entire maintenance process until you arrive at what is relevant to the maintenance activities. 3. Siemens Instruments Realize Predictive Maintenance In recent years, Siemens has designed and developed many highly intelligent instrument products around the concept of predictive maintenance. 1. Sitrans DA400 pump diagnostic system As mentioned in the previous example, effective and low-cost maintenance has always been a problem for most slurry pumps that are highly abrasive during transportation. The Sitrans DA400, an instrument based on sound-sensitive diagnostics and Profibus technology launched by Siemens, can effectively provide a solution for predictive maintenance of pumps. Pump leakage usually occurs in turbulent flow areas, especially when cavitation occurs under high pressure conditions. At this time, sound waves or ultrasonic waves will be generated, which can be detected by installing sensors outside the valve. Sound level is a unit of measurement for leakage. The online diagnostic system Sitrans DA400 (Figure 2) can fully meet these requirements. The acoustic emission sensor is installed outside the valve and is highly sensitive to leakage signals but insensitive to pump operation signals. Appropriate processing of the signal enables the measurement results to automatically adapt to changes in load conditions, such as pressure or number of strokes. The operator only needs to set the alarm value. The SITRANS DA400 sound-sensitive detection system provides an excellent solution for detecting the operating status of the suction valve and discharge valve of the reciprocating diaphragm coal slurry pump. SITRANS DA400 uses the detection of the presence of cavitation and the corresponding sound level of the pump's operating noise to diagnose whether the valve's opening and closing action is intact. Conversely, if cavitation is present in a defective valve, it can be identified by a periodic increase in sound level. ； The valve under inspection detects this increase in sound level through SITRANS DA400 ； This detection can be done in a dedicated ultrasonic range. This filters out noise caused by pump operation and valve closing actions. Sitrans DA400 condition-based maintenance requires monitoring the condition of the device or operating unit. Its advantages are:: Ø Damage is discovered before the device fails, Ø A shutdown repair plan can be developed, Ø Costly damage has been repaired before it occurs, Ø The service life of parts is extended, Ø Automatic inspection of the device or unit. According to more than half a year of on-site experimental observations, the average maintenance time of the diaphragm pump installed with Sitrans DA400 is reduced by about 2/3 compared to the two groups of diaphragm pumps that are not installed, which means the maintenance cost is reduced by 2/3 (as shown in Figure 3). At the same time, * * Extends the service life of the equipment. 2. Sipart PS2 Intelligent Valve Positioner Since the intelligent valve positioner is installed on the pneumatic control valve, its working environment is relatively harsh. Factors such as ambient temperature, pipeline vibration, etc. will adversely affect the normal operation of the intelligent valve positioner. In the design process of the intelligent valve positioner, these unfavorable factors are taken into consideration and some of its own diagnostic functions are designed. At the same time, the pneumatic valve itself has been working in a harsh environment for a long time, and there will also be problems such as corrosion and wear. Therefore, if the diagnostic function of the intelligent valve positioner can be used to realize predictive maintenance of the valve, it is also an issue that many users are now concerned about. The fault diagnosis of the regulating valve by the intelligent valve positioner is completed through additional sensors. The microprocessor inside the positioner collects data from various sensors at regular intervals, and performs calculations, comparisons, analysis, and configuration on various data, and outputs alarm signals through the configuration software for abnormal information. The diagnosis of the regulating valve by the intelligent valve positioner is divided into online diagnosis and offline diagnosis. Online diagnostics include: Stroke accumulation, number of stroke direction changes, number of alarms, dead zone self-adjustment, valve baseline position (such as: Wear of the valve seat), hours of operation at the highest and lowest temperatures, number of operations of the piezoelectric valve, valve positioning time, actuator leakage, noise diagnosis, etc. Offline diagnostics include: The sealing, static characteristics, step transition and frequency response characteristics of the actuator and air circuit. In addition, the intelligent electrical valve positioner SIPART PS2 provides more additional monitoring functions, and the status display is derived from the monitoring of fault signals. Fault signals are classified in a "traffic light" manner, represented by green, yellow and red wrenches (PDM software), as shown in Figure 4: Ø Maintenance required (green wrench) Ø Maintenance urgently needed (yellow wrench) Ø Imminent failure or malfunction (red wrench) This allows the user to detect in advance a major failure of the valve or actuator before a major failure occurs, thus avoiding system shutdown. Through the indication of fault signals, such as damage to the actuator diaphragm, lagging action, etc., users can use appropriate maintenance strategies to ensure system reliability. 3. Sitrans CLS300 capacitive level switch is used in industries such as pharmaceuticals, fertilizers, non-ferrous metals, petrochemicals, etc., because many measurement media themselves have strong adhesion, wear and other reasons, thus adding a lot of additional maintenance costs. Therefore, Siemens has launched a capacitive level meter with Profibus bus, which analyzes key data such as the amount of material loaded on the field probe (change in zero point capacitance), wear of the probe (change in cumulative capacitance), and transmits a large amount of diagnostic information to the host computer through the field bus to help users specify predictive maintenance plans (Figure 5). * * It saves the user's maintenance costs and also extends the actual service life of the equipment due to the reduced number of replacement parts. This function is difficult to achieve for instruments that can only output switching signals. 4. Siwarex weighing system is different from weighing system in the traditional sense. Siwarex weighing system is actually a digital weighing solution fully integrated in Simatic system. Compared with traditional weighing systems, the Siwarex system breaks through the limitations of communication and system structure, and demonstrates huge technical advantages in terms of system measurement accuracy, control speed, construction cycle, and long-term maintenance volume. Due to the relatively complex structure of the weighing system and the large amount of maintenance, leading weighing technology manufacturers have long been considering how to design a solution that can not only reduce costs, simplify the structure of the weighing system, but also improve the overall performance of the weighing system. Siemens' Simatic-based weighing solution is undoubtedly a feasible solution to meet new requirements. The new Siwarex system provides a very conceptual and ideal tool for most users. Use the event recording function to record the weighing process and status in real time, and analyze abnormal events. At the same time, the diagnostic buffer will record and save the last 100 pieces of diagnostic information. By long-term and continuous monitoring of key parameters of the load cells and cables, we can obtain pre-diagnostic information about the weighing system (Figure 6) to avoid sudden and expensive equipment shutdown failures and reduce maintenance and repair costs. These functions provided by Siwarex enable the evaluation of process data.