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With the introduction of smart instruments equipped with predictive maintenance functions, this article uses the predictive maintenance features of control valves, transmitters, and pH meters as examples to illustrate the application of predictive maintenance in instrument maintenance, and shares cases of how to endow ordinary instruments with such predictive capabilities. Regarding safe production and efficient manufacturing, users are increasingly concerned about how to shift equipment maintenance from a reactive approach to a proactive one. This article introduces the general categories of maintenance work, the advantages of predictive maintenance, and illustrates the predictive maintenance functions of smart instruments using control valves, transmitters, and pH meters as examples. Finally, it presents examples of the display of predictive maintenance information as well as renovation projects. The production process often requires to proceed continuously, but the equipment involved in this process (including process equipment, mechanical equipment, electrical equipment, instruments, and control systems) may experience failures during operation, leading to interruptions in production. To minimize the losses caused by process interruptions, it is essential in companies to have a team dedicated to maintenance work. Maintenance work is generally divided into three categories: 1. Post-maintenance. Post-maintenance refers to repairing equipment only after it has failed; it is a form of passive maintenance. This approach of not performing repairs unless there is a failure is the simplest method, and it is also the one most commonly used by users at present. The problem is that failures result in long equipment downtime and significant losses ； High spare parts inventory costs are high ； Overtime labor costs. 2. Preventive maintenance: Preventive maintenance is typically a type of maintenance work that is carried out in advance according to a plan; it involves performing maintenance before the equipment fails, such as through time-based maintenance plans or condition-based maintenance plans. Just as the worn-out parts of a certain type of pumping equipment need to be replaced after 12 months of operation, this can be considered a time-based maintenance plan ； Maintaining a certain metallurgical furnace every 50 batches can be considered a condition-based maintenance plan. Usually, he can avoid the equipment downtime that results from repairing the equipment only when it fails, but planned maintenance also leads to the problem of \"over-maintenance\", where the equipment is repaired even when there is no fault. 3. Predictive maintenance: Predictive maintenance includes equipment condition monitoring, fault diagnosis, fault prediction, maintenance decision support, and maintenance activities. It is a form of proactive maintenance, also known as predictive maintenance. It involves conducting fault diagnosis based on equipment condition monitoring, and providing issue descriptions, predictive information, and repair recommendations for those faults. Among its advantages, predictive maintenance helps to reduce the long downtime caused by reactive maintenance, improves the reliability and availability of equipment, extends its service life, and better implements the principle of \"safety first, prevention first.\" It overcomes the randomness associated with preventive maintenance by being highly targeted – different approaches are adopted depending on the condition of the equipment. This leads to reduced operational and maintenance costs, less workload, and lower labor intensity, which in turn facilitates staff reduction and increased efficiency as well as improved economic benefits. http://yunrun.com.cn/upload/201912/23/201912230031529543.png For automated instruments, various manufacturers have successively introduced intelligent instruments with predictive maintenance functions. This article will selectively use several products as examples to illustrate the predictive maintenance features of control valves, transmitters, and pH meters. Predictive maintenance of control valves: Fault diagnosis of control valves through intelligent valve positioners. The microprocessor inside the locator periodically collects data from various sensors, performs calculations, comparisons, analyses, and configurations, and outputs any abnormal information in the form of alarm signals. The diagnosis of control valves by intelligent valve positioners can be divided into online diagnosis and offline diagnosis. Online diagnosis includes travel accumulation, number of direction changes, number of fault alarms, dead zone self-adjustment, valve baseline position (such as seat wear, operating hours at maximum and minimum temperatures, valve positioning time, actuator leakage, noise diagnosis, etc.). Offline diagnosis includes: the sealing performance, static characteristics, step response, and frequency response characteristics of the actuator and air circuit. Siemens’ intelligent electrical valve positioner SIPART PS2 offers a number of additional monitoring functions, with the status display being based on the monitoring of fault signals. This allows users to detect major failures in valves or actuators in advance, preventing system disruptions due to such failures. By identifying fault signals such as damage to the actuator diaphragm or delay in operation, users can employ appropriate maintenance strategies to ensure system reliability. Predictive maintenance of transmitters: Generally, predictive maintenance can be achieved through fault diagnosis of intelligent transmitters. The fault diagnosis functions of intelligent transmitters include: diagnosis of clogged pressure conduits, failure of pressure sensors, failure of temperature sensors, memory errors in sensor modules, excessive ambient temperatures, and excessive pressures. The 3051S transmitter developed by Emerson Process Management utilizes statistical process control technology to detect abnormalities in process environments. It uses high-precision, fast-sampling sensors (more than 20 times per second) to measure pressure, converting the minor variations and noise in pressure into statistical data such as average values, standard deviations, rates of change in standard deviations, and various ratios between these values. The basic principle of this technology is that when the equipment is operating normally, any changes in the noise characteristics or variation patterns of the various dynamic process stages indicate that significant changes are occurring or are about to occur in the processes, process equipment, or transmitters. For example, in flow measurement applications, if one or both of the pressure tap tubes of a differential pressure transmitter become clogged, the standard deviation at a specific flow rate will change significantly, allowing the blockage in the pressure tap tubes to be detected. In the past, users could only detect pipeline blockages when the transmitter output failed to change in response to actual pressure changes after the blockage had persisted for some time. In other specific applications, significant changes in the liquid content within wet gases, as well as unstable abnormal phenomena prior to flame extinction in metallurgical furnaces, can also be detected. Predictive maintenance of pH meters: The predictive maintenance function of pH meters includes features such as condition monitoring, fault diagnosis, fault prediction, as well as information display and transmission. Fault diagnosis is carried out on the basis of condition monitoring, and it encompasses many aspects, including wiring self-diagnosis, the solution under test self-diagnosis, sensor self-diagnosis, and so on. The wiring self-diagnosis function is mainly used for diagnosing open circuits and short circuits in the relevant wiring ； The self-diagnosis function of the solution under test mainly involves the detection of temperature and pH value anomalies. The most important and complex function, however, is the sensor’s self-diagnosis function, whose diagnostic aspects include the following four points: 1. Measurement of the glass electrode impedance ； 2. Determination of reference electrode impedance ； 3. Determination of pH sensor sensitivity ； 4. Determination of the pH sensor response time. http://yunrun.com.cn/upload/201912/23/201912230047207746.png Figure 1 Schematic diagram of the method for determining the half-value recovery time of a pH sensor. Figure 1 shows the schematic of this determination method; the Yokogawa TM20BG pH meter uses this approach. The curve shown in the figure represents the results obtained after cleaning the pH sensor with a cleaning agent and then placing it in the solution to be tested. pH2 is the pH value of the cleaning agent, while pH1 is the pH value of the solution being tested. The pH sensor during the AB period was cleaned with a cleaning agent, and the pH value it displayed was pH2 ； Starting from time B, the pH sensor is placed back into the solution being tested; at this point, the displayed pH value continues to rise until it stabilizes at pH1. The time it takes for the displayed pH value to increase by 1/2 (pH1 – pH2) can be referred to as the half-value recovery time t1. A time limit can be preset (ranging from 0.1 to 10 minutes); if the detected half-value recovery time t1 exceeds this limit, it can be concluded that the pH sensor has a slow response. Display of predictive information: Generally, predictive information can be displayed in two ways: either directly on the panel of the smart meter or on the operation station of the control system. 1. On the panel display of smart meters, the fault signals of the Siemens SIPART PS2 smart electrical valve positioner, as mentioned earlier, are classified in a “traffic light” manner, with green, yellow, and red icons displayed on its operation panel (see Figure 2): http://yunrun.com.cn/upload/201912/23/201912230055430883.png Figure 2: Monitoring functions on the smart electrical valve positioner SIPART PS2 ◆ Normal, no maintenance required (no icon) ◆ Requires repair (green icon) ◆ Urgent repair needed (yellow icon) ◆ On the verge of failure or already faulty (red icon) The second example is Honeywell’s APT400 series pH meters; on its LCD display panel, while the main display and auxiliary display show numerical values (for example, the main display shows the pH value while the auxiliary display shows the solution temperature), the rest of the information is presented using pictorial symbols. As shown in Figure 3, the three faces with different expressions represent three different conditions of the electrode: good, fair, and poor. When performing electrode asymmetry potential and slope checks, if the smiley face is displayed above, it indicates that the electrode is in good condition (OK) ； If an expressionless face is shown in the middle, it indicates that the electrode condition is satisfactory ； If the crying face is displayed below, it indicates that the electrode condition is poor and does not meet the required accuracy for calibration; the electrode needs to be replaced. http://yunrun.com.cn/upload/201912/23/201912230103554566.png Figure 3: Display panel of the APT400 series pH meter with pictographic symbols. The third example is the ISM® (Intelligent Sensor Management) system introduced by Mettler Toledo; an intelligent chip is installed on top of pH sensors equipped with this function. This chip can store all relevant parameters of the sensor, as well as algorithms for sensor diagnosis and predictive maintenance. Through the IEEE digital communication protocol, information such as the manufacturer of the pH sensor, its type, model, serial number, order number, and last calibration data can be transmitted to devices such as the M700 transmitter. Devices such as the M700 transmitter monitor parameters such as the impedance of glass electrodes and reference electrodes; at the same time, it can perform diagnostics based on this historical process data and provide data for predictive maintenance. All of the aforementioned testing, diagnostic, and predictive data can be displayed on the M700 transmitter’s display panel, which is an LC display with a resolution of 240×160. The sensor life display shown on this panel is already a detailed view with content similar to that displayed on a computer. Below the box showing the sensor lifespan, the first row displays a bar chart of the sensor’s lifespan; this bar chart makes it clear that the sensor still has roughly 5/6 of its lifespan left ； The second line shows the amount of time the sensor has been in use (out of an estimated 300-day lifespan, 48 days have already passed) ； When the black portion of the bar graph is filled or when 300 days of operation have elapsed, it indicates that the pH sensor’s lifespan has ended and it should be replaced ； CIP in line 4 and SIP in line 5 refer to online cleaning and online sterilization, respectively; they are terms used in the heating processes during mass production in the pharmaceutical and food industries. Since CIP/SIP cycles take a long time, and each cycle involves exposure to high temperatures for disinfection or sterilization, this has a significant impact on the lifespan of electrodes. Therefore, it is generally not allowed to exceed the predetermined number of cycles (such as 10 cycles in this example), and the system can automatically count the number of CIP/SIP cycles performed. 2. The intelligent instrument is displayed on the control system operation station; it can also transmit statistical parameters to the control system via communication protocols such as HART and FF. Using these statistical parameters, the system can detect or display changes in the process environment. If any abnormal conditions occur during the process, the system will check these values and perform predictive maintenance diagnostics; it will then send these values along with the diagnostic results to the control system’s operation station for display. Siemens’ SIWAREX weighing system is actually a digitally integrated weighing solution built into the SIMATIC control system. It overcomes the limitations associated with communication and system architecture, and offers significant technical advantages in terms of measurement accuracy, control speed, installation time, and long-term maintenance requirements compared to systems that use conventional instruments. Due to the relatively complex structure of weighing systems and the high maintenance requirements associated with them, Siemens’ SIMATIC-based weighing solutions represent a solution that meets the new demands for reducing costs, simplifying the structure of weighing systems, and improving their overall performance. The SIWAREX system uses its event logging function to record the weighing process and status in real time, analyzes abnormal events, and the diagnostic buffer stores the last 100 diagnostic messages. By conducting long-term, continuous monitoring of the key parameters of load cells and cables, we can obtain predictive diagnostic information regarding the weighing system, thereby avoiding equipment downtime and reducing maintenance costs. The AMS Intelligent Equipment Management System of Emerson Process Management includes an electronic inspection function that checks the status of the instruments on site every five minutes; any faults detected are displayed in the alarm list, alerting workers to carry out maintenance at the site. Electronic inspection complements traditional manual inspection (manual inspection checks for leaks in equipment and pipelines, while electronic inspection detects internal faults in equipment as well as changes in its configuration). When a fault occurs in the on-site instruments, it is displayed in the alarm list of the AMS intelligent device management system within 5 minutes, alerting the maintenance staff via sound and light signals. The maintenance staff can then carry out on-site repairs promptly based on the information provided in the alarm list. It reduced the duration that the fault persisted. The enhanced EDDL (Electronic Device Description Language) can be used to display comprehensive diagnostic information on process data; as shown in Figure 7, if the pressure tap of a pressure transmitter becomes clogged, its diagnostic information can be displayed during operation. Transformation examples: Some people might say that although smart meters with prediction functions are useful, we use ordinary meters – so how can we give them such prediction capabilities as well? Replacing the smart meters is certainly one option, but in some cases, modification may also be a viable solution. Here’s an example: Shagang Group’s cooling system consists of 8 water tanks, and the control valves for these tanks are equipped with DVC6020HC regulators, which are early-model products from FISHER Company. Issues such as unstable flow control in the water tanks arose due to problems with the electrical converters and inadequate positioning accuracy; as a result, the control performance of these valves failed to meet the requirements of the manufacturing process. Numerous attempts to modify and optimize the control programs proved ineffective. Subsequent modifications were carried out, replacing all the electrical converters on the original control valves with intelligent positioners FIELDVUE DVC6010PD that support the HART communication protocol. An online performance monitoring system for the valves was added, enabling the detection of problems before they affect process operations and thus facilitating predictive maintenance. However, since the system currently in use does not support the HART communication protocol, a HART multiplexer is used to read/transmit HART information. The 16 valves are connected to the ValveLink workstation, where the ValveLink software enables online management of the valves with predictive maintenance capabilities. Recommended reading: An overview of the HART protocol command format http://yunrun.com.cn/tech/2850.html