Description:

TECHNICAL FIELD OF THE INVENTION
The present invention relates to the field of predictive maintenance and remote monitoring of industrial equipment, more particularly to a system for diagnostic of health and mechanical load conditions of DC motors from remote location.

BACKGROUND OF THE INVENTION
Electric motors are the work horse of any metal industry like the steel Industry. As such the health monitoring of the motors is imperative in order to prevent any undesirable stoppage in the production line. In old mills, the health monitoring of these motors has history as old as the equipment themselves. The method adopted in earlier times have changed from maintaining the history of failure of equipment and doing the reactive maintenance or scheduled maintenance to analysis of equipment parameters for preventive or predictive maintenance.

With the advent of smart sensors in the fourth phase of industrial revolution it becomes imperative to change over to a system of maintenance based on analytics rather than reactive maintenance.

The prior art related to the field of predictive maintenance are:
US11385622B2 discloses systems and method for data collection in an industrial environment can include interpreting a plurality of sensor data values, each sensor operatively coupled to at least one of a plurality of components in the industrial environmental. In response to at least a portion of the plurality of sensor data values, determining a recognized pattern value and providing a system characterization value for the industrial system in response to the recognized pattern value.

US20210049480A1 discloses systems, methods and apparatus of predictive maintenance of automotive battery. For example, a vehicle has: an electric motor; battery configured to power at least the electric motor; one or more sensors configured to measure operating parameters of the battery; an artificial neural network configured to analyze the operating parameters of the battery as a function of time to generate a result; and at least one processor configured to generate a suggestion for a maintenance service of the battery based on the result from the artificial neural network analyzing the operating parameters of the battery. For example, the electric motor can be part of an engine starter for an internal combustion engine, or a motor for an electric vehicle.

US20200019154A1 discloses systems and methods for monitoring and remote balancing a motor are disclosed. An exemplary system may include a plurality analog sensors operationally coupled to a motor, an analog switch communicatively coupled to at least one of analog sensors, wherein a first analog sensor input is a trigger channel and a second is an input channel. The analog switch may digitally derive a relative phase between the trigger channel and the input channel using a phase-lock loop (PLL) band-pass tracking filter on at least one of the analog sensor channels to obtain one of slow-speed rotations per minute (RPMs) or phase information for the motor.

JP2024019175A discloses a method and system for data collection in an industrial environment and a method and system for using the collected data. The system is connected to a first machine and includes a platform that includes a computing environment connected to a local data collection system having a first sensor signal and a second sensor signal from the first machine. a first sensor in the local data collection system, a second sensor in the local data collection system, a plurality of inputs including a first input of the first sensor and a second input of the second sensor; and a crosspoint switch having an output. The plurality of outputs includes a state in which the first output switches between delivering the first sensor signal and delivering the second sensor signal, and the first sensor signal from the first output and the second sensor signal from the second output.

The health monitoring of the motors is imperative in order to prevent any undesirable stoppage in the production line. As such the method adopted in earlier times have changed from maintaining the history of failure of equipment and doing the reactive maintenance to analysis of equipment parameters for predictive maintenance.

In line with the current trends of predictive maintenance, there is a need for a diagnostic system for health and mechanical load conditions of DC motors from remote location to be design and implement for DC motors which are installed at inaccessible locations such that it is not possible to intermittently check the conditions of these motors. The conditions like roller jamming and other mechanical overload are only detectable after the failure of motor or burning of motor cable.

OBJECT OF THE INVENTION
It is an object of the present invention to overcome the shortcomings of the prior art.

It is an object of the present invention to provide a system for diagnosing the health and mechanical load conditions of DC motors from a remote location.

Yet another object of the present invention is to improve motor protection and prevent failures by monitoring various parameters such as current, voltage, temperature, vibrations, and sparking, and using machine learning for intelligent analysis and predictive maintenance.

Yet another object of the present invention is to reduce unscheduled stoppage or breakdown.

Still another object of the present invention is to enable switching over from old motor protection systems to analytics-based motor protection systems.

SUMMARY OF THE INVENTION
The following disclosure presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the present invention. It is not intended to identify the key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concept of the invention in a simplified form as a prelude to a more detailed description of the invention presented later.

In one aspect of the present invention there is provided a system for diagnostic of health and mechanical load conditions of DC motors from remote locations, the system comprising:
a set of DC motors driven by analog convertor;
Smart sensors configured to collect data;
A cloud server for storing and analyzing the data;
A controller configured to process data collected by said smart sensors;
An edge computing device for preprocessing data and sent to the cloud server; and
Dashboards for displaying trending of armature current, voltage, vibrations, temperature, and time of generation of digital output of the light sensor;
wherein the system is configured to analyze data and determine the mechanical load condition, bearing conditions, and commutator condition with respect to intensity and frequency of sparking.

In another aspect of the present invention there is provided the system further comprises a vibration and temperature sensor (1) installed on the pedestal bearing of the motor;
a spark sensor (2) installed on a commutator surface, connected to a signal isolator, and interfaced with a voltage and current transducer; and
electrical panels (3) interfaced with an analog input card and a commutator spark detection sensor interfaced with a digital input card.

Other aspects, advantages, and salient features of the invention will become apparent to those skilled in the art from the following detailed description, which, taken in conjunction with the annexed drawings, discloses exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
The above and other aspects, features and advantages of the embodiments of the present disclosure will be more apparent in the following description taken in conjunction with the accompanying drawings, in which:

Figure 1 depicts a schematic diagram of a system for diagnostic of health and mechanical load conditions of DC motors from remote locations in accordance with an embodiment of the present invention.

Figure 2 depicts photos of hardware installed in the field of a system for diagnostic of health and mechanical load conditions of DC motors from remote locations in accordance with an embodiment of the present invention.

Figure 3 depicts motor over current trip logic of a system for diagnostic of health and mechanical load conditions of DC motors from remote locations in accordance with an embodiment of the present invention.

Figure 4 depicts motor over load logic of a system for diagnostic of health and mechanical load conditions of DC motors from remote locations in accordance with an embodiment of the present invention.

Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and may not have been drawn to scale. For example, the dimensions of some of the elements in the figure may be exaggerated relative to other elements to help to improve understanding of various exemplary embodiments of the present disclosure. Throughout the drawings, it should be noted that like reference numbers are used to depict the same or similar elements, features, and structures.

DETAILED DESCRIPTION OF THE PRESENT INVENTION
The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various embodiments of the present disclosure as defined by the claims and their equivalents. It includes various specific details to assist in that understanding, but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the various embodiments described herein can be made without departing from the scope and spirit of the present disclosure. In addition, descriptions of well-known functions and constructions may be omitted for clarity and conciseness.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments belong. Further, the meaning of terms or words used in the specification and the claims should not be limited to the literal or commonly employed sense but should be construed in accordance with the spirit of the disclosure to most properly describe the present disclosure.

The terminology used herein is for the purpose of describing particular various embodiments only and is not intended to be limiting of various embodiments. As used herein, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising" used herein specify the presence of stated features, integers, steps, operations, members, components, and/or groups thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, members, components, and/or groups thereof.

The present disclosure will now be described more fully with reference to the accompanying drawings, in which various embodiments of the present disclosure are shown.

In one embodiment present invention describes a system for diagnostic of health and mechanical load conditions of DC motors from remote locations, the system comprising:
a set of DC motors driven by analog convertor;
Smart sensors configured to collect data;
A cloud server for storing and analyzing the data;
A controller configured to process data collected by said smart sensors;
An edge computing device for preprocessing data and sent to the cloud server; and
Dashboards for displaying trending of armature current, voltage, vibrations, temperature, and time of generation of digital output of the light sensor;
wherein the system is configured to analyze data and determine the mechanical load condition, bearing conditions, and commutator condition with respect to intensity and frequency of sparking.

According to one implementation of the present invention, the system further comprises a vibration and temperature sensor (1) installed on the pedestal bearing of the motor;
a spark sensor (2) installed on a commutator surface, connected to a signal isolator, and interfaced with a voltage and current transducer; and
electrical panels (3) interfaced with an analog input card and a commutator spark detection sensor interfaced with a digital input card.

In an another embodiment the present invention describes to a system for diagnostic of health and mechanical load conditions of dc motors from remote location comprises of smart sensors for capturing armature current and voltage, bearing vibration and temperature and commutator sparking through light sensors. The data collected by smart sensors are sent to cloud server via controller and edge computing device. The trending of armature current, voltage, vibrations, temperature and time of generation of digital output of the light sensor is depicted through dashboards. Through Machine Learning, the system has been made intelligent to point the mechanical load condition, the bearing conditions and commutator condition with respect to intensity and frequency of sparking. The system with the trip logic for stopping the motor when current crosses the threshold value beyond the threshold time has helped in preventing any failure of motor or cable and thus reduces unscheduled stoppage or breakdown. The predictive analysis through captured data for vibrations, current, temperature and voltage has helped in increasing the equipment availability by reducing the motor /cable failures. The system for diagnostic of health and mechanical load conditions of DC motors from remote location has helped in switching over from the old motor protection system to analytics based motor protection system.

The health monitoring of motors is imperative in order to prevent any undesirable stoppage in the production line. The method adopted in earlier times have changed from maintaining the history of failure of equipment and doing the reactive maintenance or scheduled maintenance to analysis of equipment parameters for preventive or predictive maintenance. The advent of smart sensors and edge gateway devices have made it possible to send the data to cloud servers and remotely analyse the equipment parameters for assessing the health of motors and predict the condition-based maintenance schedule.

In the present invention for diagnostic of health and mechanical load conditions of DC motors from remote location , a set of DC motors were selected which were driven by analog convertor. The location of some of these motors were such that it not possible to intermittently check the conditions of these motors. The protection available for these motors was only against the absolute overload. The conditions like roller jamming and other mechanical overload were only detectable after the failure of motor or burning of motor cable.

As is depicted in the Figure -1 schematic for health condition monitoring of DC Motors, the current of the motors and armature voltage of these motors from thyristor convertor is captured through the analog signal convertor and then sent to RIO panel and to PLC. Vibration sensor data is sent through RS485 to PLC and spark sensor DI to PLC. The PLC with Profinet master sends the data to On-premise server. All data from PLC is logged to local SCADA and displayed through it.. The data from PLC is pushed to cloud server through LAN port using edge device ECU-1051. Alerts generated through AI/ML for failure prediction based current and voltage data is sent to PLC through ECU-1051. The system architecture is based on sensorization of the analog signals from the motors and sending it to cloud through edge devices. With this invention for diagnostic health monitoring of the motors it is possible to continuously monitor the current and voltage graph. Logic is incorporated to protect the motor in case the current goes beyond a set threshold limit and beyond set threshold time. Low Voltage vs high current graph logic indicating mechanical jamming/ mechanical load is incorporated to prevent motor failure due to over loads.

Figure-1 depicts, the field sensors (1: V1-V10) for vibration & temperature measurement of the motor bearings, isolators I/P (2) for tapping the motor voltage and current from the electrical panels interfaced with analog input card and commutator spark detection sensor interfaced with the digital input card (3). All these signals are sent to the On-premise server through communication devices (4: Profinet and Ethernet switch) and to the cloud server (6) through Edge device (5: ECU-1051). The trip logic is incorporated inside the PLC programme and if activated, the motor is tripped through relay output generated for motor trip. The undesirable load conditions are analysed in the could server and associated analytics based on trends of the parameters like voltage, current, vibrations, temperature & commutator spark. Cloud based dashboards are available through IP address and Local dashboards through On-premise server (7). The local dashboard acts as redundant system for internet based cloud based dashboards.

Figure 2 depicts the hardware of the system installed in the field. The number (1) shows the vibration & temperature sensor installed on the pedestal bearing of the motor. The number (2) shows the spark sensor on the commutator surface and its connection interfaced with signal isolator. The signal isolator is also interfaced with voltage and current transducer. The number (3) shows the electrical panels interfaced with analog input card and commutator spark detection sensor interfaced with the digital input card.

Figure 3 depicts the logic for motor over current protection. The number (1) is actual motor current, (2) motor threshold current , (3) threshold time (4) digital output (5) digital input to reset the latch and (6) for alarm generation.

Figure 4 depicts the logic for motor load protection. The number (1) is actual motor current, (2) motor threshold current, (3) actual voltage (4) threshold voltage (5) threshold time (6) digital output generation.

The following advantage can be list out from the present invention
The system for diagnostic of health and mechanical load conditions of DC motors has its usefulness in bringing about the paradigm shift from reactive maintenance to predictive maintenance. The process of monitoring and predictive maintenance planning through such system can reduce the maintenance cost and improve the mill availability.

Industrial Applicability:
Similar system for diagnostic of health and mechanical load conditions of DC motors from remote location can be implemented for DC motors installed in Industry and in vulnerable position where intermittent inspection of motor is not possible.
, Claims:
1. A system for diagnostic of health and mechanical load conditions of DC motors from remote locations, the system comprising:
a set of DC motors driven by an analog convertor;
plurality of smart sensors configured to collect data;
a cloud server for storing and analyzing the data;
a controller configured to process data collected by said smart sensors;
an edge computing device for preprocessing data and sent to the cloud server; and
dashboards for displaying trending of armature current, voltage, vibrations, temperature, and time of generation of digital output of the light sensor;
wherein the system is configured to analyze data and determine the mechanical load condition, bearing conditions, and commutator condition with respect to intensity and frequency of sparking.

2. The system as claimed in claim 1, further comprising trip logic for stopping the motor when current crosses a threshold value beyond a threshold time.

3. The system as claimed in claim 1, wherein the system capture data for vibrations, current, temperature, and voltage increases equipment availability by reducing motor/cable failures.

4. The system as claimed in claim 1, wherein the system further comprising:
a vibration and temperature sensor (1) installed on the pedestal bearing of the motor;
a spark sensor (2) installed on a commutator surface, connected to a signal isolator, and interfaced with a voltage and current transducer; and
electrical panels (3) interfaced with an analog input card and a commutator spark detection sensor interfaced with a digital input card.