Author: Oleg Zakharov, chief specialist of the expert group of PRANA Predictive Analytics and Remote Monitoring System, ROTEC JSC
Monitoring-and-diagnostics data (M&D) form the basis for determining the technical condition of equipment and developing repair schedules.
Due to the modern advancement of materials, technologies and control systems, as well as developments in key industries such as instrumentation, power engineering and hardware, we have many options for equipping the observed facility with various sensors and systems that allow for the output of a huge array of information to control panels, control rooms and situational centers.
For example, Figure 1 demonstrates the equipping of a power transformer allowing for the online tracking of significant defects and malfunctions in virtually all units and elements.
In addition, Figure 2 shows specific manufacturers and suppliers (domestic and foreign) of primary sensors and M&D systems.
Seemingly, there are no problems in terms of information on the equipment’s technical state - it’s more than sufficient. But there are two key points.
First, the variety of sensors and systems for signals processing and issuance, and often the lack of the complementary ability to combine them, makes the owner bear additional financial costs due to the limitations of their joint application (either software and hardware dependence, or the need for the complete replacement of existing hardware equipping during modernization).
Second, operational personnel are not able to constantly record and analyze the incoming information flow. At the same time, the occurrence and degree of degradation is physically impossible to assess quickly, since the trends of the parameters may not be critical for a long time, and at some point the process will begin to snowball and it will be too late to do anything. As a compensating action, the main emphasis in operational preventive maintenance is on pre-alarms and all kinds of technological and other protections, as well as on emergency automation.
When alarms and protections are triggered and equipment is turned off, two possible scenarios arise:
Thus, there is a need for the formation of a qualitatively new model of the operational-cycle management of the production assets of power facilities, power-grid complexes, transport-infrastructure and industrial conglomerates, etc. This contributes to the:
World experience shows that the remote online monitoring of facilities with the participation of expert groups, where a particular area (equipment) specialist “leads” their own grouping of different remote objects (assesses the presence and degree of degradation, gives recommendations to operational personnel) is more appropriate from the standpoint of economics and intellectual property. At the same time, the software and hardware involved in statistical-data processing are supplemented by expert assessment. Thus, digital technologies make it possible to carry out such monitoring without a large staff of experts at the company, and without incurring significant costs associated with paying for equipment, personnel training, etc. Thus, the following digitalization definition can be given. Digitalization is the process of creating and maintaining the originally-defined functionality of the information space:
Important is the fact that the information space should have a clear practical benefit for a particular user.
The predictive analytics and remote monitoring system PRANA is a man-machine system of domestic production. It has a remote monitoring center (RMC), consisting of a dispatch center and an expert group. The basis of its universal software and hardware “shell” is the well-known apparatus of mathematical statistics MSET – Hotelling criterion T2 (multidimensional generalization of any number of independent variables with a different coefficient of influence).
The information space of the object predictive analytics and remote monitoring system is formed in full accordance with the above definition (see Figure 3).
Figure 4 shows the architecture of the PRANA information space.
Figure 4 shows that formed and constantly-replenished arrays of archival data, which are the basis for any repair-quality analysis, investigation of technological faults, operational maintenance, etc., form the basis of PRANA. Any operating modes (for example, “thermal field”, “time regression”, etc.) are visualized using software and hardware. In addition, the system makes it possible to monitor objects of any distance and complexity. This achieves the objectivity and transparency level of the results of the analysis and assessment of the technical condition of the monitoring object, which ensures the economic efficiency of management.
The system’s universality lies in the fact that any number of any input signals gives only one integral parameter at the output, which determines the change in the object’s technical condition. In other words, PRANA can be used to monitor and evaluate the technical condition of any type of equipment, from a single engine to a whole engineering and manufacturing complex, including engines, ovens, transformers, foundations, tanks and other equipment by comparing the actual and model T at each point in time.2The main condition for correct comparison is the correspondence between the number and name of the parameters being compared in the “cross-section” (data array) and those found in the model. The input-signal sources can be both archival and current values of the parameters being monitored.
The system detects changes in technical condition automatically, and is able to do so throughout the equipment life cycle:
It is important to note that in addition to methodology, the correct selection of input parameters and correctly-constructed and functioning mode models is needed.
Proceeding directly to power transformers, the maximum information is provided due to:
Historically, when the PRANA System was created in 2015, it was originally designed to monitor thermal-power facilities. Later, in 2018, it was decided to expand it to encompass power-grid facilities as well. Therefore, the initial experience in the monitoring of power-grid complex facilities is a matter of interest, including:
Preliminary applied findings from initial experience in the monitoring of electric-power facilities:
Additional conclusions based on the results of the online direct monitoring of CHP transformers are as follows:
The experience of the described predictive analytics and remote monitoring system shows what must be introduced and improved – and how – for the digitalization of fuel-and-energy complex facilities in general and the power-grid complex in particular. Main conclusions:
1Systems for automation and integration with the lower-level server of Predictive Analytics and Remote Monitoring System PRANA require fine-tuning; (-) – significant retrofitting with primary sensors and diagnostic tools is required.