Industrial Servo Motor YASKAWA AC SERVO MOTOR 100V 2.2A 0.318N.m SGM-01V3NT14

Activities to Improve Fine Motor
Coordination in Hands
These are activities to increase the coordination in your hands. Your therapist will instruct you on how often you should do each activity.
· Shuffle and deal cards. · Use an ipad or smart phone.
· Trace a design with a pen. · Lace shoes and tie them.
· Make a chain of paper clips. · Stack coins or wrap them in coin wrappers.
· Pick up small beans and put them in a cup or straw.
· Pick up marbles and hold as many as you can in your hand.
· Pick up hooks and eyes and put them in a medicine bottle. You can buy hooks and eyes in sewing sections of stores.
· Play a game with small pegs or playing pieces.
· Pick up coins and place them in a coin purse or wallet.
· Bounce a small ball and catch it.
· Put clothes pins on the side of a cup. · Practice buttoning.
· Screw and unscrew nuts and bolts. · Practice putting on and taking off jewelry, such as a watch or bracelet.
· Pick up toothpicks with a tweezers. · String beads or buttons.
· Fold, crumple or tear paper or tissue. · Sew with needle and thread.
· String beads or macaroni on a string. · Cut out coupons.

SIMILAR PRODUCTS

Induction motors are used worldwide as the “workhorse” in industrial applications. Although, these electromechanical devices are highly reliable, they are susceptible to many types of faults. Such fault can became catastrophic and cause production shutdowns, personal injuries, and waste of raw material. However, induction motor faults can be detected in an initial stage in order to prevent the complete failure of an induction motor and unexpected production costs. Accordingly, this thesis presents two methods to detect induction motor faults. The first method is a motor fault diagnostic method that identifies two types of motor faults: broken rotor bars and inter-turn short circuits in stator windings. These two types of faults represent 40 to 50% of all reported faults. Moreover, this method identifies the motor fault’s severity through the identification of the number of broken bars and the number of turns involved in an interturn short. The second method is a motor fault monitoring method that classifies the
operating condition of an induction motor as healthy or faulty. The faulty condition represents any number of broken bars. This method has two major advantages. First, this is a robust technique, which is trained with datasets generated by time-stepping finite element methods in order to monitor faults of real induction motors in operation. Thus, the high cost associated with destructive tests to generate the training sets is not required.

Second, it will be demonstrated here that this method, which is trained with simulated data of only one motor, can be used to monitor faults of real motors even with different design specifications. This establishes the scalability of this method. Both methods are validated through experimental tests.

OTHER SUPERIOR PRODUCTS